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Machine Learning Model Evaluation

If we were to list the technologies that have revolutionized and changed our lives for the better, then Machine Learning will occupy the top spot. This cutting-edge technology is used in a wide variety of applications in day-to-day life. ML has become an integral component in most of the industries like Healthcare, Software, Manufacturing, Business and aims to solve many complex problems while reducing human effort and dependency. This it does by accurately predicting solutions for problems and various applications.Generally there are two important stages in machine learning. They are Training & Evaluation of the model. Initially we take a dataset to feed to the machine learning model, and this process of feeding the data to our Designed ML model is called Training. In the training stage, the model learns the behavior of data, capable of handling different forms of data to better suit the model, draws conclusion from the data and finally predicts the end results using the model.This technique of training helps a user to know the output of the designed machine learning model for the given problem, the inputs given to the model, and the output that is obtained at the end of the model.But as machine learning model engineers, we might doubt the applicability of the model for the problem and have questions like, is the developed Machine learning model best suited for the problem, how accurate the model is, how can we say this is the best model that suits the given problem statement and what are the measures that describe model performance?In order to get clarity on the above questions, there is a technique called Model Evaluation, that describes the performance of the model and helps us understand if the designed model is suitable for the given problem statement or not.This article helps you to know, the various measures involved in calculating performance of a model for a particular problem and other key aspects involved.What is Model Evaluation?This technique of Evaluation helps us to know which algorithm best suits the given dataset for solving a particular problem. Likewise, in terms of Machine Learning it is called as “Best Fit”. It evaluates the performance of different Machine Learning models, based on the same input dataset. The method of evaluation focuses on accuracy of the model, in predicting the end outcomes.Out of all the different algorithms we use in the stage, we choose the algorithm that gives more accuracy for the input data and is considered as the best model as it better predicts the outcome. The accuracy is considered as the main factor, when we work on solving different problems using machine learning. If the accuracy is high, the model predictions on the given data are also true to the maximum possible extent.There are several stages in solving an ML problem like collection of dataset, defining the problem, brainstorming on the given data, preprocessing, transformation, training the model and evaluating. Even though there are several stages, the stage of Evaluation of a ML model is the most crucial stage, because it gives us an idea of the accuracy of model prediction. The performance and usage of the ML model is decided in terms of accuracy measures at the end.Model Evaluation TechniquesWe have known that the model evaluation is an Integral part in Machine Learning. Initially, the dataset is divided into two types, they are “Training dataset” and “Test dataset”. We build the machine learning model using the training dataset to see the functionality of the model. But we evaluate the designed Model using a test dataset, which consists of unseen or unknown samples of the data that are not used for training purposes. Evaluation of a model tells us how accurate the results were. If we use the training dataset for evaluation of the model, for any instance of the training data it will always show the correct predictions for the given problem with high accuracy measures, in that case our model is not adequately effective to use.  There are two methods that are used to evaluate a model performance. They are  Holdout Cross ValidationThe Holdout method is used to evaluate the model performance and uses two types of data for testing and training. The test data is used to calculate the performance of the model whereas it is trained using the training data set.  This method is used to check how well the machine learning model developed using different algorithm techniques performs on unseen samples of data. This approach is simple, flexible and fast.Cross-validation is a procedure of dividing the whole dataset into data samples, and then evaluating the machine learning model using the other samples of data to know accuracy of the model. i.e., we train the model using a subset of data and we evaluate it with a complementary data subset. We can calculate cross validation based on the following 3 methods, namely Validation Leave one out cross validation (LOOCV) K-Fold Cross ValidationIn the method of validation, we split the given dataset into 50% of training and 50% for testing purpose. The main drawback in this method is that the remaining 50% of data that is subjected to testing may contain some crucial information that may be lost while training the model. So, this method doesn’t work properly due to high bias.In the method of LOOCV, we train all the datasets in our model and leave a single data point for testing purpose. This method aims at exhibiting lower bias, but there are some chances that this method might fail because, the data-point that has been left out may be an outlier in the given data; and in that case we cannot produce better results with good accuracy. K-fold cross validation is a popular method used for evaluation of a Machine Learning model. It works by splitting the data into k-parts. Each split of the data is called a fold. Here we train all the k subsets of data to the model, and then we leave out one (k-1) subset to perform evaluation on the trained model. This method results in high accuracy and produces data with less bias.Types of Predictive ModelsPredictive models are used to predict the outcomes from the given data by using a developed ML model. Before getting the actual output from the model, we can predict the outcomes with the help of given data. The prediction models are widely used in machine learning, to guess the outcomes from the data before designing a model. There are different types of predictive models: Classification model Clustering model Forecast model Outlier modelA Classification model is used in decision making problems. It separates the given data into different categories, and this model is best suited to answer “Yes” or “No” questions. It is the simplest of all the predictive models.Real Life Applications: Projects like Gender Classification, Fraud detection, Product Categorization, Malware classification, documents classification etc.Clustering models are used to group the given data based on similar attributes. This model helps us to know how many groups are present in the given dataset and we can analyze what are the groups, which we should focus on to solve the given problem statement.Real Life Applications: Projects like categorizing different people present in a classroom, types of customers in a bank, identifying fake news, spam filter, document analysis etc.A forecast model learns from the historical data in order to predict the new data based on learning. It majorly deals with metric values.Real Life Applications: Projects like weather forecast, sales forecast, stocks prices, Heart Rate Monitoring etc.Outlier model focuses on identifying irrelevant data in the given dataset. If the data consists of outliers, we cannot get good results as the outliers have irrelevant data. The outliers may have categorical or numerical type of data associated with them.Real Life Applications: Major applications are used in Retail Industries, Finance Industries, Quality Control, Fault Diagnosis, web analytics etc.Classification MetricsIn order to evaluate the performance of a Machine Learning model, there are some Metrics to know its performance and are applied for Regression and Classification algorithms. The different types of classification metrics are: Classification Accuracy Confusion Matrix Logarithmic Loss Area under Curve (AUC) F-MeasureClassification AccuracyClassification accuracy is similar to the term Accuracy. It is the ratio of the correct predictions to the total number of Predictions made by the model from the given data.We can get better accuracy if the given data samples have the same type of data related to the given problem statement. If the accuracy is high, the model is more accurate and we can use the model in the real world and for different types of applications also.If the accuracy is less, it shows that the data samples are not correctly classified to suit the given problem.Confusion MatrixIt is a NxN matrix structure used for evaluating the performance of a classification model, where N is the number of classes that are predicted. It is operated on a test dataset in which the true values are known. The matrix lets us know about the number of incorrect and correct predictions made by a classifier and is used to find correctness of the model. It consists of values like True Positive, False Positive, True Negative, and False Negative, which helps in measuring Accuracy, Precision, Recall, Specificity, Sensitivity, and AUC curve. The above measures will talk about the model performance and compare with other models to describe how good it is.There are 4 important terms in confusion matrix: True Positives (TP): The cases in which our predictions are TRUE, and the actual output was also TRUE. True Negatives (TN): The cases in which our predictions are FALSE, and the actual output was also FALSE. False Positives (FP): The cases in which our predictions are TRUE, and the actual output was FALSE. False Negative (FN): The cases in which our predictions are FALSE, and the actual output was TRUE. The accuracy can be calculated by using the mean of True Positive and True Negative values of the total sample values. It tells us about the total number of predictions made by the model that were correct. Precision is the ratio of Number of True Positives in the sample to the total Positive samples predicted by the classifier. It tells us about the positive samples that were correctly identified by the model.  Recall is the ratio of Number of True Positives in the sample to the sum of True Positive and False Negative samples in the data.  F1 ScoreIt is also called as F-Measure. It is a best measure of the Test accuracy of the developed model. It makes our task easy by eliminating the need to calculate Precision and Recall separately to know about the model performance. F1 Score is the Harmonic mean of Recall and Precision. Higher the F1 Score, better the performance of the model. Without calculating Precision and Recall separately, we can calculate the model performance using F1 score as it is precise and robust.Sensitivity is the ratio of Number of actual True Positive Samples to the sum of True Positive and False Positive Samples. It tells about the positive samples that are identified correctly with respect to all the positive data samples in the given data. It is also called as True Positive Rate.  Specificity is also called the True Negative Rate. It is the ratio of the Number of True Negatives in the sample to the sum of True negative and the False positive samples in the given dataset. It tells about the number of actual Negative samples that are correctly identified from the given dataset. False positive rate is defined as 1-specificity. It is the ratio of number of False Positives in the sample to the sum of False positive and True Negative samples. It tells us about the Negative data samples that are classified as Positive, with respect to all Negative data samples.For each value of sensitivity, we get a different value of specificity and they are associated as follows:   Area Under the ROC Curve (AUC - ROC)It is a widely used Evaluation Metric, mainly used for Binary Classification. The False positive rates and the True positive rates have the values ranging from 0 to 1. The TPR and FPR are calculated with different threshold values and a graph is drawn to better understand about the data. Thus, the Area Under Curve is the plot between false positive rate and True positive rate at different values of [0,1].Logarithmic LossIt is also called Log Loss. As we know, the AUC ROC determines the model performance using the predicted probabilities, but it does not consider model capability to predict the higher probability of samples to be more likely positive. This technique is mostly used in Multi-class Classification. It is calculated to the negative average of the log of correctly predicted probabilities for each instance. where, y_ij, indicates whether sample i belongs to class j or not p_ij, indicates the probability of sample i belonging to class j Regression MetricsIt helps to predict the state of outcome at any time with the help of independent variables that are correlated. There are mainly 3 different types of metrics used in regression. These metrics are designed in order to predict if the data is underfitted or overfitted for the better usage of the model.  They are:-  Mean Absolute Error (MAE)  Mean Squared Error (MSE) Root Mean Squared Error (RMSE)Mean Absolute Error is the average of the difference of the original values and the predicted values. It gives us an idea of how far the predictions are from the actual output. It doesn’t give clarity on whether the data is under fitted or over fitted. It is calculated as follows:The mean squared error is similar to the mean absolute error. It is computed by taking the average of the square of the difference between original and predicted values. With the help of squaring, large errors can be converted to small errors and large errors can be dealt with.  It is computed as follows. The root mean squared error is the root of the mean of the square of difference of the predicted and actual values of the given data. It is the most popular metric evolution technique used in regression problems. It follows a normal distribution and is based on the assumption that errors are unbiased. It is computed using the below formulae.Bias vs VarianceBias is the difference between the Expected value and the Predicted value by our model. It is simply some assumptions made by the model to make the target function easier to learn. The low bias indicates fewer assumptions, whereas the high bias talks about more assumptions in the target data. It leads to underfitting of the model.Variance takes all types of data including noise into it. The model considers the variance as something to learn, and the model learns too much from the trained data, and at the end the model fails in giving out accurate results to the given problem statement. In case of high variance, the model learns too much and it can lead to overfitting of the model. ConclusionWhile building a machine learning model for a given problem statement there are two important stages, namely training and testing. In the training stage, the models learn from the data and predict the outcomes at the end. But it is crucial that predictions made by the developed model are accurate. This is why the stage of testing is the most crucial stage, because it can guarantee how accurate the results were to implement for the given problem.  In this blog, we have discussed about various types of Evaluation techniques to achieve a good model that best suits a given problem statement with highly accurate results. We need to check all the above-mentioned parameters to be able to compare our model performance as compared to other models.
Machine Learning Model Evaluation
Harsha
Harsha

Harsha Vardhan Garlapati

Blog Writer at KnowledgeHut

Harsha Vardhan Garlapati is a Data Science Enthusiast and loves working with data to draw meaningful insights from it and further convert those results and implement them in business growth. He is a final year undergraduate student and passionate about Data Science. He is a smart worker, passionate learner,  an Ice-Breaker and loves to participate in Hackathons to work on real time projects. He is a Toastmaster Member at S.R.K.R Toastmasters Club, a Public Speaker, a good Innovator and problem solver.

Posts by Harsha Vardhan Garlapati

Machine Learning Model Evaluation

If we were to list the technologies that have revolutionized and changed our lives for the better, then Machine Learning will occupy the top spot. This cutting-edge technology is used in a wide variety of applications in day-to-day life. ML has become an integral component in most of the industries like Healthcare, Software, Manufacturing, Business and aims to solve many complex problems while reducing human effort and dependency. This it does by accurately predicting solutions for problems and various applications.Generally there are two important stages in machine learning. They are Training & Evaluation of the model. Initially we take a dataset to feed to the machine learning model, and this process of feeding the data to our Designed ML model is called Training. In the training stage, the model learns the behavior of data, capable of handling different forms of data to better suit the model, draws conclusion from the data and finally predicts the end results using the model.This technique of training helps a user to know the output of the designed machine learning model for the given problem, the inputs given to the model, and the output that is obtained at the end of the model.But as machine learning model engineers, we might doubt the applicability of the model for the problem and have questions like, is the developed Machine learning model best suited for the problem, how accurate the model is, how can we say this is the best model that suits the given problem statement and what are the measures that describe model performance?In order to get clarity on the above questions, there is a technique called Model Evaluation, that describes the performance of the model and helps us understand if the designed model is suitable for the given problem statement or not.This article helps you to know, the various measures involved in calculating performance of a model for a particular problem and other key aspects involved.What is Model Evaluation?This technique of Evaluation helps us to know which algorithm best suits the given dataset for solving a particular problem. Likewise, in terms of Machine Learning it is called as “Best Fit”. It evaluates the performance of different Machine Learning models, based on the same input dataset. The method of evaluation focuses on accuracy of the model, in predicting the end outcomes.Out of all the different algorithms we use in the stage, we choose the algorithm that gives more accuracy for the input data and is considered as the best model as it better predicts the outcome. The accuracy is considered as the main factor, when we work on solving different problems using machine learning. If the accuracy is high, the model predictions on the given data are also true to the maximum possible extent.There are several stages in solving an ML problem like collection of dataset, defining the problem, brainstorming on the given data, preprocessing, transformation, training the model and evaluating. Even though there are several stages, the stage of Evaluation of a ML model is the most crucial stage, because it gives us an idea of the accuracy of model prediction. The performance and usage of the ML model is decided in terms of accuracy measures at the end.Model Evaluation TechniquesWe have known that the model evaluation is an Integral part in Machine Learning. Initially, the dataset is divided into two types, they are “Training dataset” and “Test dataset”. We build the machine learning model using the training dataset to see the functionality of the model. But we evaluate the designed Model using a test dataset, which consists of unseen or unknown samples of the data that are not used for training purposes. Evaluation of a model tells us how accurate the results were. If we use the training dataset for evaluation of the model, for any instance of the training data it will always show the correct predictions for the given problem with high accuracy measures, in that case our model is not adequately effective to use.  There are two methods that are used to evaluate a model performance. They are  Holdout Cross ValidationThe Holdout method is used to evaluate the model performance and uses two types of data for testing and training. The test data is used to calculate the performance of the model whereas it is trained using the training data set.  This method is used to check how well the machine learning model developed using different algorithm techniques performs on unseen samples of data. This approach is simple, flexible and fast.Cross-validation is a procedure of dividing the whole dataset into data samples, and then evaluating the machine learning model using the other samples of data to know accuracy of the model. i.e., we train the model using a subset of data and we evaluate it with a complementary data subset. We can calculate cross validation based on the following 3 methods, namely Validation Leave one out cross validation (LOOCV) K-Fold Cross ValidationIn the method of validation, we split the given dataset into 50% of training and 50% for testing purpose. The main drawback in this method is that the remaining 50% of data that is subjected to testing may contain some crucial information that may be lost while training the model. So, this method doesn’t work properly due to high bias.In the method of LOOCV, we train all the datasets in our model and leave a single data point for testing purpose. This method aims at exhibiting lower bias, but there are some chances that this method might fail because, the data-point that has been left out may be an outlier in the given data; and in that case we cannot produce better results with good accuracy. K-fold cross validation is a popular method used for evaluation of a Machine Learning model. It works by splitting the data into k-parts. Each split of the data is called a fold. Here we train all the k subsets of data to the model, and then we leave out one (k-1) subset to perform evaluation on the trained model. This method results in high accuracy and produces data with less bias.Types of Predictive ModelsPredictive models are used to predict the outcomes from the given data by using a developed ML model. Before getting the actual output from the model, we can predict the outcomes with the help of given data. The prediction models are widely used in machine learning, to guess the outcomes from the data before designing a model. There are different types of predictive models: Classification model Clustering model Forecast model Outlier modelA Classification model is used in decision making problems. It separates the given data into different categories, and this model is best suited to answer “Yes” or “No” questions. It is the simplest of all the predictive models.Real Life Applications: Projects like Gender Classification, Fraud detection, Product Categorization, Malware classification, documents classification etc.Clustering models are used to group the given data based on similar attributes. This model helps us to know how many groups are present in the given dataset and we can analyze what are the groups, which we should focus on to solve the given problem statement.Real Life Applications: Projects like categorizing different people present in a classroom, types of customers in a bank, identifying fake news, spam filter, document analysis etc.A forecast model learns from the historical data in order to predict the new data based on learning. It majorly deals with metric values.Real Life Applications: Projects like weather forecast, sales forecast, stocks prices, Heart Rate Monitoring etc.Outlier model focuses on identifying irrelevant data in the given dataset. If the data consists of outliers, we cannot get good results as the outliers have irrelevant data. The outliers may have categorical or numerical type of data associated with them.Real Life Applications: Major applications are used in Retail Industries, Finance Industries, Quality Control, Fault Diagnosis, web analytics etc.Classification MetricsIn order to evaluate the performance of a Machine Learning model, there are some Metrics to know its performance and are applied for Regression and Classification algorithms. The different types of classification metrics are: Classification Accuracy Confusion Matrix Logarithmic Loss Area under Curve (AUC) F-MeasureClassification AccuracyClassification accuracy is similar to the term Accuracy. It is the ratio of the correct predictions to the total number of Predictions made by the model from the given data.We can get better accuracy if the given data samples have the same type of data related to the given problem statement. If the accuracy is high, the model is more accurate and we can use the model in the real world and for different types of applications also.If the accuracy is less, it shows that the data samples are not correctly classified to suit the given problem.Confusion MatrixIt is a NxN matrix structure used for evaluating the performance of a classification model, where N is the number of classes that are predicted. It is operated on a test dataset in which the true values are known. The matrix lets us know about the number of incorrect and correct predictions made by a classifier and is used to find correctness of the model. It consists of values like True Positive, False Positive, True Negative, and False Negative, which helps in measuring Accuracy, Precision, Recall, Specificity, Sensitivity, and AUC curve. The above measures will talk about the model performance and compare with other models to describe how good it is.There are 4 important terms in confusion matrix: True Positives (TP): The cases in which our predictions are TRUE, and the actual output was also TRUE. True Negatives (TN): The cases in which our predictions are FALSE, and the actual output was also FALSE. False Positives (FP): The cases in which our predictions are TRUE, and the actual output was FALSE. False Negative (FN): The cases in which our predictions are FALSE, and the actual output was TRUE. The accuracy can be calculated by using the mean of True Positive and True Negative values of the total sample values. It tells us about the total number of predictions made by the model that were correct. Precision is the ratio of Number of True Positives in the sample to the total Positive samples predicted by the classifier. It tells us about the positive samples that were correctly identified by the model.  Recall is the ratio of Number of True Positives in the sample to the sum of True Positive and False Negative samples in the data.  F1 ScoreIt is also called as F-Measure. It is a best measure of the Test accuracy of the developed model. It makes our task easy by eliminating the need to calculate Precision and Recall separately to know about the model performance. F1 Score is the Harmonic mean of Recall and Precision. Higher the F1 Score, better the performance of the model. Without calculating Precision and Recall separately, we can calculate the model performance using F1 score as it is precise and robust.Sensitivity is the ratio of Number of actual True Positive Samples to the sum of True Positive and False Positive Samples. It tells about the positive samples that are identified correctly with respect to all the positive data samples in the given data. It is also called as True Positive Rate.  Specificity is also called the True Negative Rate. It is the ratio of the Number of True Negatives in the sample to the sum of True negative and the False positive samples in the given dataset. It tells about the number of actual Negative samples that are correctly identified from the given dataset. False positive rate is defined as 1-specificity. It is the ratio of number of False Positives in the sample to the sum of False positive and True Negative samples. It tells us about the Negative data samples that are classified as Positive, with respect to all Negative data samples.For each value of sensitivity, we get a different value of specificity and they are associated as follows:   Area Under the ROC Curve (AUC - ROC)It is a widely used Evaluation Metric, mainly used for Binary Classification. The False positive rates and the True positive rates have the values ranging from 0 to 1. The TPR and FPR are calculated with different threshold values and a graph is drawn to better understand about the data. Thus, the Area Under Curve is the plot between false positive rate and True positive rate at different values of [0,1].Logarithmic LossIt is also called Log Loss. As we know, the AUC ROC determines the model performance using the predicted probabilities, but it does not consider model capability to predict the higher probability of samples to be more likely positive. This technique is mostly used in Multi-class Classification. It is calculated to the negative average of the log of correctly predicted probabilities for each instance. where, y_ij, indicates whether sample i belongs to class j or not p_ij, indicates the probability of sample i belonging to class j Regression MetricsIt helps to predict the state of outcome at any time with the help of independent variables that are correlated. There are mainly 3 different types of metrics used in regression. These metrics are designed in order to predict if the data is underfitted or overfitted for the better usage of the model.  They are:-  Mean Absolute Error (MAE)  Mean Squared Error (MSE) Root Mean Squared Error (RMSE)Mean Absolute Error is the average of the difference of the original values and the predicted values. It gives us an idea of how far the predictions are from the actual output. It doesn’t give clarity on whether the data is under fitted or over fitted. It is calculated as follows:The mean squared error is similar to the mean absolute error. It is computed by taking the average of the square of the difference between original and predicted values. With the help of squaring, large errors can be converted to small errors and large errors can be dealt with.  It is computed as follows. The root mean squared error is the root of the mean of the square of difference of the predicted and actual values of the given data. It is the most popular metric evolution technique used in regression problems. It follows a normal distribution and is based on the assumption that errors are unbiased. It is computed using the below formulae.Bias vs VarianceBias is the difference between the Expected value and the Predicted value by our model. It is simply some assumptions made by the model to make the target function easier to learn. The low bias indicates fewer assumptions, whereas the high bias talks about more assumptions in the target data. It leads to underfitting of the model.Variance takes all types of data including noise into it. The model considers the variance as something to learn, and the model learns too much from the trained data, and at the end the model fails in giving out accurate results to the given problem statement. In case of high variance, the model learns too much and it can lead to overfitting of the model. ConclusionWhile building a machine learning model for a given problem statement there are two important stages, namely training and testing. In the training stage, the models learn from the data and predict the outcomes at the end. But it is crucial that predictions made by the developed model are accurate. This is why the stage of testing is the most crucial stage, because it can guarantee how accurate the results were to implement for the given problem.  In this blog, we have discussed about various types of Evaluation techniques to achieve a good model that best suits a given problem statement with highly accurate results. We need to check all the above-mentioned parameters to be able to compare our model performance as compared to other models.
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Machine Learning Model Evaluation

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Combining Models – Python Machine Learning

Machine Learning is emerging as the latest technology these days, and is solving many problems that are impossible for humans. This technology has extended its wings into diverse industries like Automobile, Manufacturing, IT services, Healthcare, Robotics and so on. The main reason behind using this technology is that it provides more accurate solutions for problems, simplifies tasks and eases work processes. It automates the world with its applications that are helpful for many organizations and for the well-being of people. This technology uses the input data to develop a model, and further predicts the outcomes to know the performance of the model.Generally, we develop machine learning models to solve a problem by using the given input data. When we work on a single algorithm, we are unable to distinguish the performance of the model for that particular statement, as there is nothing to compare it against. So, we feed the input data to other machine learning algorithms and then compare them with each other to know which is the best algorithm that suits the given problem. Every algorithm has its own mathematical computation and significance to deal with a specific problem to bring out the best results at the end.Why do we combine models?While dealing with a specific problem with a machine learning algorithm we sometimes fail, because of the poor performance of the model. The algorithm that we have used may be well suited to the model, but we still fail in getting better outcomes at the end. In this situation, we might have many questions in our mind. How can we bring out better results from the model? What are the steps to be taken further in the model development? What are the hidden techniques that can help to develop an efficient model?To overcome this situation there is a procedure called “Combining Models”, where we mix one or two weaker machine learning models to solve a problem and get better outcomes. In machine learning, the combining of models is done by using two approaches namely “Ensemble Models” & “Hybrid Models”.Ensemble Models use multiple machine learning algorithms to bring out better predictive results, as compared to using a single algorithm. There are different approaches in Ensemble models to perform a particular task. There is another model called Hybrid model that is flexible and helps to create a more innovative model than an Ensemble model. While combining models we need to check how strong or weak a particular machine learning model is, to deal with a particular problem.What are Ensemble Methods?An Ensemble is made up of things that are grouped together, that take up a particular task. This method combines several algorithms together to bring out better predictive results, as compared to using a single algorithm. The objective behind the usage of an Ensemble method is that it decreases variance, bias and improves predictions in a developed model. Technically speaking, it helps in avoiding overfitting.The models that contribute to an Ensemble are referred to as the Ensemble Members, which may be of the same type or different types, and may or may not be trained on the same training data.In the late 2000s, adoption of ensembles picked up due in part to their huge success in machine learning competitions, such as the Netflix Prize and other competitions on Kaggle.These ensemble methods greatly increase the computational cost and complexity of the model. This increase comes from the expertise and time required to train and maintain multiple models rather than a single model.Ensemble models are preferred because of two main reasons; namely Performance & Robustness. The ensemble methods majorly focus on improving the accuracy of the model by reducing variance component of the prediction error and by adding bias to the model.Performance helps a Machine Learning model to make better predictions. Robustness reduces the spread or dispersion of the prediction and model performance.The goal of a supervised machine learning algorithm is to have “low bias and low variance”.The Bias and the Variance are inversely proportional to each other i.e., if the bias is low then the variance is high, else the bias is high then the variance is low.We explicitly use ensemble methods to seek better predictive performance, such as lower error on regression or higher accuracy for classification. They are also further used in Computer vision and are given utmost importance in academic competitions also.Decision TreesThis type of algorithm is commonly used in decision analysis and operation Research, and it is one of the mostly used algorithms in the context of Machine Learning.The decision tree algorithm aims to produce better results for small and large amounts of data, which are taken as input data and fed to the model. These algorithms are majorly used in decision making problem statements.The decision tree algorithm is a tree like structure consisting of nodes at each stage. The top of the tree is the Root Node which describes the main problem that we deal with, and there are Sub Nodes which act as classes or labels for the data given in the dataset. The Leaf Node is the last layer of the decision tree, representing the outcomes or values of the problem.The tree structure is extended with a number of nodes till a perfect prediction is made from the given data using the model. Decision tree algorithms are used in classification as well as regression problems. This algorithm is widely used in machine learning to solve problems, and the main advantage of this model is that we can have 2 or more outputs, from which we can select the most suitable one for the given problem.These can operate on both small and large amounts of data. Decisions taken using this algorithm are often fast and accurate. In machine learning the different types of Decision Tree algorithms includeClassification and Regression Tree (CART)Decision stumpChi-squared automatic interaction detection (CHAID)Types of Ensemble MethodsEnsemble methods are used to improve the accuracy of the model by reducing the bias and variance. These methods are widely used in dealing with Classification and Regression Problems. In ensemble method, several models combine together to form one reliable model that results in improving accuracy at the end.Ensemble methods are widely classified into the following types to exhibit better performance of the model. They are:BaggingBoostingStackingThese ensemble methods are broadly classified into four categories, namely “Sequential methods”, “Parallel methods”, “Homogeneous Ensemble” and “Heterogeneous Ensemble”. They help us to differentiate the performance and accuracy of models for a problem.Sequential methods generate sequential base learners who are data dependent. Here the new data we take as input to the model is dependent on the previous data, and the data which is mislabeled previously by the model is tuned with weights to get better accuracies at the end. This technique is possible in “BOOSTING”, for example in Adaptive Boosting (AdaBoost).Parallel methods generate parallel order base learners in which the data is independent. This independence of the base learners on the data significantly reduces the error with the application of averages. This technique is possible in “STACKING”, for example in Random Forest.A Homogenous ensemble is a combination of the same type of classifiers. Even though the dataset consists of different classifiers, this ensemble technique makes a model that best suits a given problem. This type of technique is computationally expensive and is suitable for solving large datasets. “BAGGING” & “BOOSTING” are the popular methods that exhibit homogeneous ensemble.Heterogeneous ensemble is a combination of different types of classifiers, in which each classifier is based on the same data. This method works on small datasets. “STACKING” comes in this category.BaggingBagging is a short form of Bootstrap Aggregating, used to improve the accuracy of the model. It is used when dealing with problems related to Classification and Regression. This technique improves the accuracy of the model by reducing variance, and helps to prevent the overfitting of the model. Bagging can be applied with any type of method in machine learning, but generally it is implemented using Decision Trees.Bagging is an ensemble technique, in which several models are grouped together to make one single reliable model to improve the accuracy. In the technique of bagging, we fit several independent models together and average their predictions to get a model that results in low variance and high accuracy to the model.Bootstrapping is a sampling technique, where we obtain the data in the form of samples. The samples are derived from the whole population with the help of replacement procedure. The sampling technique with the help of replacement method helps the learners to make the selection procedure randomized. Now the base learning algorithm is run across the samples to complete the procedure for better results.Aggregation is a technique in bagging that helps to incorporate all the possible outcomes of the predictions and randomizes the outcomes at the end. Without the usage of aggregation, the predictions will not be that accurate, because all the outcomes that are obtained at the end of the model are not taken into consideration. Thus, the aggregation is used based on the probability bootstrapping procedures or on the basis of all outcomes of the predictive models.Bagging is an advantageous procedure in Machine Learning, as it combines all the weak base learners that come together to form a single strong learner which is more stable. This technique reduces variance, thereby increasing the accuracy to the model. It prevents overfitting of the model. The limitation for bagging is that it is computationally expensive. When the proper procedure for bagging is established, we should not ignore bias as it fails in obtaining better results at the end.Random Forest ModelsIt is a supervised machine learning algorithm, which is flexible and widely used because of its simplicity and diversity. It produces great results without hyper-parameter tuning.In the term “Random Forest”, the “Forest” refers to a group of decision trees or an ensemble of decision trees, usually trained with the method of “Bagging”. We know that the method of bagging is the combination of learning models that increases the overall result.Random forest is used for classification and regression problems. It builds many decision trees and combines them together to get a more accurate and stable prediction at the end of the model.Random forest adds additional randomness to the model, while growing the trees. Instead of finding the most important feature at the time of splitting a node, the random forest model searches for the best feature among a random subset of features. Thus in random forest, only a random subset of features is taken into consideration by the algorithm for node splitting.Random forest has the quality of measuring the relative importance of each feature on the prediction. In order to use the random forest algorithm, we import a tool “Sklearn”, which measures features importance by looking at the amount of tree nodes used to reduce the impurity across all the trees in the forest.The benefits of using random forest include the following:The training time is less compared to other algorithms.Runs efficiently on a large dataset, and predicts output with high accuracy.When a large proportion of data is missing it also maintains accuracy.It is flexible to apply and outcomes are obtained easily.BoostingBoosting is an ensemble technique, which converts the weak machine learning models into strong models. The main goal of this technique is to reduce bias and variance of a model to improve accuracy. This technique learns from the previous predictor mistakes of data to make better predictions in future by improving the performance of the model.It is a stack like structure in which the weak learners are placed at the bottom and the strong learners are placed at the top. In the stack, the learners at the upper layers initially learn from the weak learners by applying some sort of modifications to the previous techniques.It exists in many forms, that includes XGBoost (Extreme Gradient Boosting), Gradient Boosting, Adaptive Boosting (AdaBoost).AdaBoost makes use of weak learners that are in the form of decision trees, which includes one split normally known as decision stumps. The main decision stumps of Adaboost comprises of observations carrying similar weights.Gradient Boosting follows the sequential addition of predictors to an ensemble, each correcting the previous one. Without changing the weights of incorrect classified observations like Adaboost, this Gradient boosting technique places a new predictor based on the residual errors made by the previous predictors in the generated model.XGBoost is called as Extreme Gradient Boosting. It is designed in order to show better speed and performance of the machine learning model, that we developed. XGBoost technique is an implementation of Gradient Boosted Decision Trees. Generally, normal boosting techniques are very slow as they are in sequential form of training, so XGBoost technique is widely used to have good computational speed and to show better model performance.Simple Averaging / Weighted MethodIt is a technique to improve the accuracy of the model, mainly used for regression problems. It is based on the weights of the model multiplied with the actual instance values in the given problem. This method produces some consistent results that are reliable and help to get a better understanding about the outcomes of the given problem.In the case of a simple averaging method, average predictions are calculated for every instance of the test dataset. It can reduce the overfitting of the model, and is mainly suitable for regression problems as it consists of numerical data. It creates a smoother regression model at the end by reducing the effect of overfitting. The technique of simple averaging is like calculating the mean of the given values.The weighted averaging method is a slight modification to the simple averaging method, in which the prediction values are multiplied with the weight factor and sum up all the multiplied values for every instance. We then calculate the average. We assume that the predicted values are in the range of 0 to 1.StackingThis method is a combination of multiple regression or classifier techniques with a meta-regressor or meta-classifier. Stacking is different from bagging and boosting. Bagging and boosting models work mainly on homogeneous weak learners and don’t consider heterogeneous learners, whereas stacking works mainly on heterogeneous weak learners, and consists of different algorithms altogether.The bagging and boosting techniques combine weak learners with the help of deterministic algorithms, whereas the stacking method combines the weak base learners with the help of a meta-model. As we defined earlier, when using stacking, we learn from several weak base learners and combine them together by training with a meta-model to predict the results that are predicted by the weak learners used in the model.Stacking results in a pile-like structure, in which the lower-level output is used as the input to the next layer. In the same way the stack increases from maximum error rate at the bottom to the minimum error rate area at the top. The top layer in the stack has good prediction accuracy compared to the lower levels. The aim of stacking is to produce a low bias model for accurate results for a given problem.BlendingIt is a technique similar to the stacking approach, but uses only the validation set from the training set of the model to make predictions. The validation set is also called a holdout set.The blending technique uses a holdout set to make predictions for the given problem. With the help of holdout set and the predictions, a model is built which will run across the test set. The process of blending is explained below:Train dataset is divided into training and validation setsThe model is fitted on to the training setPredictions are made on the validation set and the test setNow the validation set and the predictions are used as features to build a new modelThis developed model is used to make final predictions on the test set and on the meta-features.The stacking and blending techniques are useful to improve the performance of the machine learning models. They are used to minimize the errors to get good accuracy for the given problem.Voting Voting is the easiest ensemble method in machine learning. It is mainly used for classification purposes. In this technique, the first step is to create multiple classification models using a training dataset. When the voting is applied to regression problems, the prediction is made with the average of multiple other regression models.In the case of classification there are two types of voting,Hard Voting  Soft VotingThe Hard Voting ensemble involves summing up the votes for crisp class labels from other models and predicting the class with the most votes. Soft Voting ensemble involves summing up the predicted probabilities for class labels and predicting the class label with the largest sum probability.In short, for the Regression voting ensemble the predictions are the averages of contributing models, whereas for Classification voting ensemble, the predictions are the majority vote of contributing models.There are other forms of voting like “Majority Voting” and “Weighted Voting”. In the case of Majority Voting, the final output predictions are based on the number of votes it gets. If the count of votes is high, that model is taken into consideration. In some of the articles this method is also called as “Plurality Voting”.Unlike the technique of Majority voting, the weighted voting works based on the weights to increase the importance of one or more models. In the case of weighted voting, we count the prediction of the better models multiple times.ConclusionIn order to improve the performance of weak machine learning models, there is a technique called Ensembling to improve or boost the accuracy of the model. It is comprised of different techniques, helpful for solving different types of regression and classification problems.
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Combining Models – Python Machine Learning

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How to get datasets for Machine Learning?

Datasets are the repository of information that is required to solve a particular type of problem. Also called data storage areas, they help users to understand the essential insights about the information they represent. Datasets play a crucial role and are at the heart of all Machine Learning models.  Machine Learning without data sets will not exist because ML depends on data sets to bring out relevant insights and solve real-world problems. Machine learning uses algorithms that comb through data sets and continuously improve the machine learning model.  Quality data is therefore important to ensure the efficacy of a machine learning model. Datasets are often related to a particular type of problem and machine learning models can be built to solve those problems by learning from the data. Datasets help users uncover insights before actually applying the machine learning model to it.  Many datasets are available online for learners who are starting off on building machine learning models. Alternatively, we also can make our own datasets.  Every problem statement we are dealing with comprises of data, which helps us better understand the problem and draw better insights from data by applying ML methods. In the real world, datasets are huge. So, you may have tons and tons of data that represents a particular problem. Datasets may also be confidential as they may contain sensitive information pertaining to a product, organization or government.   Data is not available in a specific format.  Dataset files may be in the form of excel sheets containing rows and columns, bunch of images, videos and audios, in the form of Text like words, sentences and paragraphs, in the form of numbers or values, messages, chats, statuses and in the form of different files like word, txt, pdf, xml and so on. Data can be related to sales of a company, weather reports, income of a company, types of manufacturing products, salary paid to each employee, customers count for a particular item, monthly savings of an employee, frequent visits of a person to a particular place, statistics of any type of industry, quality performance check of a particular item, type of projects a company deals with, etc. Data is defined according to the problem it represents.  Machine Learning Datasets  In Machine Learning, a dataset plays a key role in understanding the problem statement given by a user. A dataset is a repository of information, a collection of instances that help a user to better understand something. A dataset is used to draw better insights and get a clear picture of a particular problem statement. In Machine learning, a dataset is used as input for the machine learning model that has been developed to offer predictions based on the data.  The more data we feed a machine learning model, the better it works and more accurate it gets. If you are a beginner, there are many data sets available that you can make use of to enhance your machine learning skills.  Open-source repositories like Kaggle, UCI, Google etc. can help users to get started with Machine Learning. Open Dataset Finders To solve any problem in data science, be it in the field of Machine Learning, Deep Learning, or Artificial Intelligence, one needs a dataset that can be input into the model to derive insights. A technology has no significance without data. In the real world, data is not open source, as it is confidential and may contain very sensitive information related to an item, user or product. But raw data is available as open source for beginners and learners who wish to learn technologies associated with data. This raw data may or may not be the exact match of the real-time data. But it is a great resource for   users/learners to get better connected with the data and draw insights from it by applying different types of algorithms on it. The commonly used sites from where learners can access datasets to practice their machine learning skills include:   Kaggle  UCI Machine Learning Repository Machine Learning Datasets for Data Science Beginners Data Science, a field that encompasses machine learning, artificial intelligence, deep learning, data mining and more, has seen an unprecedented growth in the past decade.  The sole reason for this growth has been the explosion of data that we have seen in the past few years. Tons and tons of data are being generated each day and organizations have realized the vast potential that this data holds in terms of fueling innovation and predicting market trends and customer preferences.  Data science and its associated fields use algorithms, processes, and other modern tools and techniques to draw insights from vast amounts of structured and unstructured data. Data science has been consistently rated as being among the hottest job trends that is both lucrative and allows growth opportunities.  If you are a learner or an experienced IT professional wanting to learn about data science, then there are several resources available online that help you get access to datasets and polish your machine learning skills. These include:  Iris dataset  Loan Prediction Dataset  Boston Housing Dataset  Wine quality Dataset  Big Mart Sales Dataset  Time Series Analysis Dataset  Beginners of machine learning are often advised to work on Regression and Classification Problems. To make a career in data science and to know more about Machine Learning models or algorithm functionality, it is important to have a grasp of the basics of Math concepts like Statistics, Probability, Linear Algebra, and Calculus. A background of Mathematics also helps users to implement algorithms on their own. It helps to better understand about the different types of implementation of complex strategies of the model and problems in the field of Data Science. Machine Learning Datasets for Natural Language Processing  Natural Language Processing is a branch of artificial intelligence and among the fastest-growing fields in machine learning.  NLP has found applications across fields like Text Classification, Speech Recognition, Language Modelling, Summarization, Image Captioning, Sentiment Analysis, Question Answering, and more. Some popular examples of NLP applications include Amazon “Alexa”, Google Assistant, and Apple’s “Siri”. The main use of NLP is smart search, summarization, classification etc., which majorly solves most of the users' problems. NLP requires a lot of data to function well. Given below are some datasets that can be used for NLP use cases. These are classified based on different types of domain areas and are as follows.  For Text Classification, the datasets are IMDB Movie Reviews, Twitter Analysis data, Sentiment 140, and Reuters Newswire Topic Classification. For Speech Recognition, the datasets are VoxForge, TIMIT Acoustic-Phonetic Continuous Speech Corpus, LibriSpeech ASR corpus etc.   For Language Modelling, the datasets are Project Gutenberg, Google 1 Billion Word Corpus etc.  For Summarization, the datasets are Legal Case Reports Dataset, TIPSTER Text summarization evaluation conference corpus etc. For Image Captioning, the datasets are Common Objects in Context (COCO), Flickr 8k, Flickr 30k etc.  For Question Answering, the datasets are Stanford Question Answering Dataset (SQuAD), Deepmind Question Answering Corpus, and Amazon question/answer Data. The above are the basic datasets to get started with the Natural Language Processing. Learners and beginners can explore these datasets and use them to build their NLP practice projects.  Machine Learning Datasets for Computer Vision and Image Processing  Computer vision (CV) is called the other “Human eye” and focuses on enabling computers to classify images the way humans do. Machines are trained with Computer vision and Image Processing techniques and used in interpreting real-world images and videos. CV helps in the visual interpretation of images and videos and is among the most widely used applications in the world of machine learning. Computer vision applications have applications right from classifying MNIST dataset of numbers to the real-world applications like Self Driving Cars. This technology is used in various industries like Medical, Automobile, robotics, etc. It can detect the objects at any given point of time and can be used in the application of CCTVs. Computer vision technology is used in mobile applications to detect a person’s images and label them further. The basic datasets required by a user to get started with Computer Vision and Image Processing are as follows. Labelme MS-COCO ImageNet LSUN VisualQA CIFAR-10 Flowers Image sourceThe above datasets are a great resource to better understand about Computer Vision and Image Processing. Machine Learning Datasets for Deep Learning Deep Learning is a core part of Machine Learning, which deals with complex problems that deal with vast amounts of data. It has been developed to mimic the neural networks of the human brain. Deep learning uses neural networks consisting of many layers to solve problems like decision making and problem solving. Generally, machine learning has two layers. One is the Input layer-- to take input from the user and the output layer-- used to show the given problem statement's end results after processing it with a ML model. But in the case of Deep Learning there are 3 layers--called Input Layer, Hidden Layer and Output Layer. Deep learning finds applications in many industries and is used to tackle many difficult problems. The datasets for Deep Learning are as follows. Yelp Review CIFAR-10 Google AudioSet Blogger Corpus Image sourceThe datasets for Deep Learning include the datasets for Computer Vision, Natural Language Processing etc., because these are all the applications and core areas of Deep Learning. Machine Learning Datasets for Finance and Economics  We can say that the technology of Machine Learning is a boon for the Finance and Economics sector, as ML applications are widely used in these two areas. ML is used in these fields as a tool for predictions of sales forecasting, business growth, goods sold, manufacturing etc. ML is also expected to predict behavior of the consumer, which is turn will help develop economic models for the growth of the company. The basic datasets in this field are as follows. Quandl IMF Data Google Trends Financial Times Market Data Image sourceThe application of Machine Learning in the fields of Finance and Economics can be further used in stock market predictions, trading in an algorithmic way, for fraud detections etc., Machine Learning Datasets for Public Government These datasets are used by the government in making economic decisions beneficial for the citizens of the nation. The Machine Learning models train the public data that can help the government policy makers to identify the trends,  population growth or decline, migration and ageing. The datasets for the public Government are as follows. Data.gov EU Open Data Portal The UK Data Services Data USA Image sourceGiven above are the basic datasets to get started with applying Machine Learning models in context to Government data, to best analyze the trends and needs of the people of a nation. Sentiment Analysis Datasets for Machine Learning  It is a part of Natural Language Processing used to analyze text for polarity, from positive to negative. This process is used in detecting the emotions in the text of the users. We can detect the different behaviors of the author/user. We can tell how the writer's article or blog is either Humorous, Depressed, Insightful, etc. The following are the basic datasets for sentiment analysis. IMDB Reviews Sentiment140 Stanford Sentiment Treebank Twitter US Airline Sentiment Sentiment analysis is mostly used in the area of classification of tweets, chats, text etc., to know the users’ behavior at that particular context of time.  Datasets for Autonomous Driving The application of Autonomous driving is a widely used application by many of the automobile industry at present, and most possibly in the future too. It is a sophisticated application, and it includes many of the technologies incorporated in it for better functioning of the system. It comprises of the latest technologies like Computer Vision, Natural Language Processing, Deep Learning, Machine Learning etc., in order to implement the complete functioning of the system. Autonomous driving application is used in self-driving cars at present, and it can be further extended to airplanes, ships etc., to provide a better experience to the user of moving from one place to the other without driving on their own. The following are the datasets of Autonomous Driving. Berkeley DeepDrive Landmarks Landmarks-v2 Open Images v5 Level 5 Pandaset Image sourceThis technology is a boon for the Automotive industry to best deal with problems like rash driving, road accidents, harmful emissions, decreased lane capacity etc. and provide users with a better and more sophisticated way to travel.  Clinical Datasets The use of Machine Learning has extended its wings into Healthcare to solve the urgent needs and requirements of many people. ML has the capability to analyze huge patient related data sets and aid doctors in coming up with faster, better and low-cost approach to providing treatments.  ML techniques in the medical field can help in identifying cancerous tumors, rare conditions, and abnormalities and help physicians make quick decisions by providing real time data on patients. The following are some of the Clinical Datasets that beginners can use to build their machine learning models.MIMIC Critical Care Database HealthData.gov Human Mortality Database SEER HCUP ML can change the way healthcare is approached. It can lead to low-cost affordable care that everyone can access.  Datasets for Recommender Systems Recommender systems help us remember the history of previously browsed sites or necessary applications in the system in a particular site. This application has found use on e-commerce and streaming sites like Flipkart, Amazon, Netflix etc., to help users search for a particular item on the site or a movie in their play list. The recommender system is built based on the user’s preferences or choices based on a particular item. It helps the user by providing smart search to display ads on frequently visited sites. Google search Engine is the biggest Recommender system is very beneficial to the users and understands user behavior in the site search. The following are some of the datasets related to Recommender systems. Amazon Review Dataset LastFM Social Network Influencer Free Music Archive Million Song Dataset Image sourceSummaryThe above discussion is all about datasets, their significance in machine learning and the associated fields of machine learning including Deep Learning, Computer vision, and Natural Language Processing. ML is revolutionizing the way we live. It has found applications in all facets of our lives from healthcare to automobiles to banking and finance. And the crux of all Machine Learning innovations are datasets. The size and quality of the dataset affects the efficiency of the machine learning model. Machine learning models with the right datasets can provide solutions to a whole range of business challenges. Knowing how to work with and implementing datasets is a must for professionals who plan to work with machine learning and data science.    
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How to get datasets for Machine Learning?

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What are the Commonly Used Machine Learning Algorithms?

Machine Learning is a sub-branch of Artificial Intelligence, used for the analysis of data. It learns from the data that is input and predicts the output from the data rather than being explicitly programmed. Machine Learning is among the fastest evolving trends in the IT industry. It has found tremendous use in sectors across industries, with its ability to solve complex problems which humans are not able to solve using traditional techniques. ML is now being used in IT, retail, insurance, government and the military. There is no end to what can be achieved with the right ML algorithm.  Machine Learning is comprised of different types of algorithms, each of which performs a unique task. Users deploy these algorithms based on the problem statement and complexity of the problem they deal with. Generally, ML algorithms are a combination of both “Mathematics and Logic” that help write new algorithms for the problem statements or to implement existing algorithms with slight modifications. If ML algorithms consume more amounts of data while solving a problem, it results in better performance. Thus, Machine Learning works on the strategy of: “The more the data that is fed as input, the better is the performance of the model at the end”  Machine Learning follows a systematic way of solving problems to get the desired results in the end. Initially, it collects data from different sources. Then it is subjected to an Exploratory Data Analysis step to get rid of unwanted data or noise hidden in the data, and to replace or remove Null values from the data to convert it into a structured format. The whole data is subjected to the ML algorithm as input to draw insights and get the desired results at the end of the given problem statement. The accuracy of the model decides the best algorithm for the data we take in at the end.Styles of ML Algorithms Most Machine Learning algorithms are categorized into three types. This categorization is based on the kind of problem the specific algorithm deals with. The categories are:   SUPERVISED   UNSUPERVISED  SEMI – SUPERVISEDIn supervised ML algorithms, the user knows both the Input and Output data before applying any algorithm on the data. In simple terms, this can be said to be an Input-Output pair of data. Here all the data is called training data, and the information consists of labels. The training process of the data is continued till the model achieves the desired accuracy. These sets of algorithms are used to predict the results from the input data.   E.g.: - Classification & Regression algorithms   In unsupervised ML algorithms, the data is not labelled, and the output is also not known. In this case, an ML model is prepared by performing some strategies like Data selection, cleaning, preprocessing, and transformation on the data to get a perfect structure to apply algorithms on the data.  E.g.: - Clustering, Dimensionality Reduction and Association Rule Learning  In semi-supervised ML algorithms, the data consists of both labelled and unlabeled examples. Here the model must learn the structures to organize the data and make predictions from the data at the end.    Overview of Machine Learning Algorithms  Apart from the Learning styles, algorithms are further grouped by similarity. Each group possesses different learning capabilities of algorithms to solve the given problem statements and produce good results at the end. A grouping of machine learning algorithms is basically done using the area of problems they deal with, in order to increase their accuracy and reliability.  ML algorithms are grouped based on whether they are numerical, categorical, boolean, grouping, classification, video, audio, images etc. The grouping brings out a clear picture of the particular type of data taken, that addresses the specific problems faced by the user. The grouping is done to ensure that the accuracy increases while using the algorithms, as compared to the previous ones. A developer can negotiate that his/her model is the best model, when he/she compares the accuracy of his model with that of the other algorithms. This comparison helps us in finding the algorithm that best suits the given problem statement.   In today’s scenario, Machine Learning algorithms have gained great significance in many industries and can be used to advantage in day-to-day life. Machine Learning is a boon for all types of industries, as it solves many difficult problems, and helps to mitigate risk and loss of human power. This technology is used in robotic process automation, satellites, space research centres, underwater areas, self-driving cars, the automotive industry, the health industry and many others.  ML can be used to solve problems of any scale—big or small. Its advantage lies in its intuitive power. The more you teach a machine learning model, the more it learns from the data and the more perfect it becomes, helping you get better output with higher accuracy. ML is now moving towards automation and this potent combination of machine learning and automation can change our lives like we have never imagined.  The application of “Re-engineering” has been evolving at present with Machine Learning. The older applications are complex and require a large amount of code to be written, whereas ML helps make applications with fewer code, written in a universal ML language that can be better understood by developers who can make the application richer by adding more to it. Regression Algorithms It tells the relationship between two variables, like an input variable that takes data into the model with some parameters and predicts the output variable. Regression algorithms are used to predict continuous values.  They are mainly used to predict the linear data in the output, from the input data we take from a user and apply it to the model. These are the simplest algorithms to implement in real-world projects as they deal with Numerical data in preparing the model. In ML, the types of regression algorithms are as follows: Linear Regression Logistic Regression Stepwise Regression Source linkInstance Based Algorithms These algorithms are based on data and training data because the model learns from activities to predict the new cases of data given by the user. They are used for decision-based problems with instances.   Here, the model learns from the training data. The new data is compared against the trained data with a similarity measure in the data to find the best match and make predictions according to the model performance in the data classification. Generally, these types of algorithms build up a database of data and are applied upon large databases. They are also called memory-based algorithms. In machine learning, the various kinds of Instance-Based Algorithms are as follows:   Image SourceRegularization Algorithms These algorithms are used to make slight changes to the model prepared by a developer. Regularization algorithms help to overcome the problem of Over-fitting of data during the training of the model. Over-fitting learns the unwanted areas from the data, its noise, etc., and at the end, it may lead to the wrong prediction in the new data given by the user.  To overcome such types of problems during the development of the model, the regularization concept is used. In machine learning, there are two types of Regularization Algorithms, and they are: L1 Regularization or Lasso Regularization L2 Regularization or Ridge Regularization Elastic Net Image sourceDecision Tree Algorithm These algorithms are best used for decision-based problem solving on the data's attributes and are represented using a tree-like structure consisting of leaves and nodes. It consists of a root node that describes the problem we are dealing with, and it is further split into leaves, with the leaf node indicating the end to the tree. The tree structure is extended with the number of nodes till a perfect prediction is made from the given data using the model.  Decision tree algorithms are best used in classification as well as regression problems. These can operate on both small amounts and large amounts of data. Decisions taken using this algorithm are often fast and accurate. In machine learning the different types of Decision Tree algorithms include Classification and Regression Tree (CART)Decision stump Chi-squared automatic interaction detection (CHAID) Image sourceBayesian Algorithms These types of algorithms are built based on the Bayes theorem. All the algorithms in this category have a probabilistic approach in solving the problems. They tell about the probability of prediction of the outcome at the end using the given data. We can draw probabilistic insights from the data.  These are the most used algorithms in stock predictions or weather predictions, done by taking in a lot of data and examining them to predict the best outcome. They can solve both the classification and regression types of problems. In machine learning, some of the different types of these algorithms are: Naïve Bayes Bayesian Belief Networks Bayesian Networks Gaussian Naïve Bayes Clustering Algorithms Image sourceThe word clustering indicates a grouping of something. Clustering algorithms contain similar types grouped or clustered together with common similarities between the data. The models in this algorithm are generally approached in a Centroid based and a Hierarchal way. They inherit structures from the older structures and find out the new ones. The data is mostly organized into groups, based on the similarity present in the structures. In ML, the different types of clustering algorithms are: K-means clustering Hierarchal clustering K-medians Clustering Image sourceAssociation Rule Learning Algorithms These algorithms explain the relationship between the data, with the help of some rules that are predefined by them. They contain a minimum threshold predefined by the user, and this process is carried out until we get the desired results. This process is held in a sequence of steps satisfying the threshold value given by the user. If the operation fails to satisfy the minimum threshold value, the given function's data is eliminated further. The rules defined by these types of algorithms help to discover commercially useful and important associations among large datasets. In ML, the different types of Association algorithms are:Apriori Algorithm Eclat Algorithm Image sourceArtificial Neural Networks Algorithms Artificial neural network algorithms are similar to the functioning of the neural networks in our brain. Generally, these algorithms fall under the category of Deep Learning, which is a core field in Machine Learning. The ANN also has a little significance in Machine Learning as it can deal with solving the problems related to Classification and Regression. In ML, the different types of algorithms in this category are:Perceptron Back-propagation Stochastic Gradient Descent Multilayer Perceptron etcImage sourceDeep Learning Algorithms They are the core part of Machine Learning algorithms and deal with large amounts of data to get better accuracy and results. These represent an updated version of Artificial Neural Networks, used to perform better computation on the given data. Deep Learning Algorithms consists of various algorithms that can solve many real-time problems, even with huge amounts of data present. These algorithms can be used in Speech Recognition, Video analysis & Recognition, Audio, and in Text formats. The different types of Deep Learning algorithms are: Convolution Neural Networks (CNN)Recurrent Neural Networks (RNN)Long Short-Term memory networks (LSTMs)  Dimensionality Reduction Algorithms These types of algorithms are quite similar to clustering algorithms in inheriting structures from the data. They are Unsupervised machine learning algorithms, and draw insights from data in an Unsupervised manner. They follow a special technique in describing data using lesser information, i.e., they can describe whole data using some pictorial representations, graphs, pie charts etc. Even though it is an Unsupervised ML algorithm, by presenting the data in a visual format we can simplify the tasks and can solve the given problems using a Supervised learning approach. These algorithms are mainly helpful in solving classification and Regression Problems. In ML the algorithms under this category are: Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA)Multidimensional Scaling (MS)  Image sourceEnsemble Algorithms These are unique algorithms from the machine learning perspective. These models comprise multiple weaker models that are independently trained. The predictions given by these weaker models are combined to form an overall prediction from the data. It requires a lot of effort to combine the weak models and form an effective model among the weaker models to generate great accuracy.  These are very popular and powerful algorithms in ML. In real-world scenarios, we use this type of algorithm a lot, as we cannot get good accuracy from the input data. These algorithms are used to boost the accuracy of the model for the better prediction of the given problem compared to other algorithms. In ML the different types of algorithms in this category are: BoostingBagging (Bootstrapped Aggregation) Adaboost Random Forest Gradient Boosting Mechanism Weighted Average (Blending) Image sourcePopularity of Machine Learning Algorithms Machine Learning Algorithms are going to revolutionize our way of life—by helping us solve not just critical problems but offering solutions to our everyday needs.  Almost all industries today use the techniques of ML to find solutions to their problems.  ML algorithms are constantly evolving, by learning and improving themselves. The algorithm’s ability to learn from the data on its own further helps in predicting better outcomes with improved accuracy.  ML deals with many problems related to Audio, Video, Text, Classification of species or items and so on. These algorithms are further extending their applications into the fields of Robotics, Health care, Automation and more. Many industries are getting automated with the use of latest technologies like Machine Learning, Artificial Intelligence, and Data science. The main use of ML algorithms is to predict the outcomes based on the given input data by creating a model that in turn produce better results. Some of the popular applications of ML are found in sales prediction, weather prediction, stocks exchange detection etc. How to study Machine Learning Algorithms Algorithms are at the heart of machine learning. There is no predefined structure or format indicating how you should study algorithms. You should, however, have a clear picture of what the algorithm is, how it works, and where and how it should be implemented.  It is important to know the Mathematics and logic behind the algorithm, as Mathematics is key to the right implementation of the algorithm to solve real-world problems. After learning math concepts, ML enthusiasts must implement the algorithm in a programming language to know how it works. At present PYTHON and R are the two programming languages that are most widely used to implement Machine Learning in the real world. You can refer to websites, blogs, or enrol in trainings, to get a clear picture of the world of ML and algorithms.  How to Run Machine Learning Algorithms To run Machine Learning algorithms, we need to have a platform to write the Script and run the script to see the outcome. We can run ML algorithms in Python IDLE, Jupyter Notebooks, Anaconda, Google Colab, Kaggle etc., but the most widely used tool by many people is Jupyter Notebooks, due to the ease of writing and running code. When writing and running ML algorithms, a series of steps are involved such as importing the required Libraries, loading the dataset from the system or any other sources, defining the model, testing the model, and predicting the outcome with better accuracy.   ConclusionThis blog provides an overview of different types of ML Algorithms, where they are used, how to study the algorithms and how they can be run, the steps involved and so on. We hope this article has helped you to get a basic idea of machine learning, if you want to pursue a career in this field.   
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What are the Commonly Used Machine Learning Algori...

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The Role of Mathematics in Machine Learning

Automation and machine learning have changed our lives. From the most technologically savvy person working in leading digital platform companies like Google or Facebook to someone who is just a smartphone user, there are very few who have not been impacted by artificial intelligence or machine learning in some form or the other;  through social media, smart banking, healthcare or even Uber.  From self – driving Cars, robots, image recognition, diagnostic assessments, recommendation engines, Photo Tagging, fraud detection and more, the future for machine learning and AI is bright and full of untapped possibilities.With the promise of so much innovation and path-breaking ideas, any person remotely interested in futuristic technology may aspire to make a career in machine learning. But how can you, as a beginner, learn about the latest technologies and the various diverse fields that contribute to it? You may have heard of many cool sounding job profiles like Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer etc., that are not just rewarding monetarily but also allow one to grow as a developer and creator and work at some of the most prolific technology companies of our times. But how do you get started if you want to embark on a career in machine learning? What education background should you pursue and what are the skills you need to learn? Machine learning is a field that encompasses probability, statistics, computer science and algorithms that are used to create intelligent applications. These applications have the capability to glean useful and insightful information from data that is useful to arrive business insights. Since machine learning is all about the study and use of algorithms, it is important that you have a base in mathematics.Why do I need to Learn Math?Math has become part of our day-to-day life. From the time we wake up to the time we go to bed, we use math in every aspect of our life. But you may wonder about the importance of math in Machine learning and whether and how it can be used to solve any real-world business problems.Whatever your goal is, whether it’s to be a Data Scientist, Data Analyst, or Machine Learning Engineer, your primary area of focus should be on “Mathematics”.  Math is the basic building block to solve all the Business and Data driven applications in the real-world scenario. From analyzing company transactions to understanding how to grow in the day-to-day market, making future stock predictions of the company to predicting future sales, Math is used in almost every area of business. The applications of math are used in many Industries like Retail, Manufacturing, IT to bring out the company overview in terms of sales, production, goods intake, wage paid, prediction of their level in the present market and much more.Pillars of Machine LearningTo get a head start and familiarize ourselves with the latest technologies like Machine learning, Data Science, and Artificial Intelligence, we have to understand the basic concepts of Math, write our own Algorithms and implement  existing Algorithms to solve many real-world problems.There are four pillars of Machine Learning, in which most of our real-world business problems are solved. Many algorithms in Machine Learning are also written using these pillars. They areStatisticsProbabilityCalculusLinear AlgebraMachine learning is all about dealing with data. We collect the data from organizations or from any repositories like Kaggle, UCI etc., and perform various operations on the dataset like cleaning and processing the data, visualizing and predicting the output of the data. For all the operations we perform on data, there is one common foundation that helps us achieve all of this through computation-- and that is Math.STATISTICSIt is used in drawing conclusions from data. It deals with the statistical methods of collecting, presenting, analyzing and interpreting the Numerical data. Statistics plays an important role in the field of Machine Learning as it deals with large amounts of data and is a key factor behind growth and development of an organization.Collection of data is possible from Census, Samples, Primary or Secondary data sources and more. This stage helps us to identify our goals in order to work on further steps.The data that is collected contains noise, improper data, null values, outliers etc. We need to clean the data and transform it into a meaningful observations.The data should be represented in a suitable and concise manner. It is one of the most crucial steps as it helps to understand the insights and is used as the foundation for further analysis of data.Analysis of data includes Condensation, Summarization, Conclusion etc., through the means of central tendencies, dispersion, skewness, Kurtosis, co-relation, regression and other methods.The Interpretation step includes drawing conclusions from the data collected as the figures don’t speak for themselves.Statistics used in Machine Learning is broadly divided into two categories, based on the type of analyses they perform on the data. They are Descriptive Statistics and Inferential Statistics.a) Descriptive StatisticsConcerned with describing and summarizing the target populationIt works on a small dataset.The end results are shown in the form of pictorial representations.The tools used in Descriptive Statistics are – Mean, Median, Mode which are the measures of Central and Range, Standard Deviation, variance etc., which are the measures of Variability.b) Inferential StatisticsMethods of making decisions or predictions about a population based on the sample information.It works on a large dataset.Compares, tests and predicts the future outcomes.The end results are shown in the probability scores.The specialty of the inferential statistics is that, it makes conclusions about the population beyond the data available.Hypothesis tests, Sampling Distributions, Analysis of Variance (ANOVA) etc., are the tools used in Inferential Statistics.Statistics plays a crucial role in Machine Learning Algorithms. The role of a Data Analyst in the Industry is to draw conclusions from the data, and for this he/she requires Statistics and is dependent on it.PROBABILITYThe word probability denotes the happening of a certain event, and the likelihood of the occurrence of that event, based on old experiences. In the field of Machine Learning, it is used in predicting the likelihood of future events.  Probability of an event is calculated asP(Event) = Favorable Outcomes / Total Number of Possible OutcomesIn the field of Probability, an event is a set of outcomes of an experiment. The P(E) represents the probability of an event occurring, and E is called an Event. The probability of any event lies in between 0 to 1. A situation in which the event E might occur or not is called a Trail.Some of the basic concepts required in probability are as followsJoint Probability: P(A ∩ B) = P(A). P(B), this type of probability is possible only when the events A and B are Independent of each other.Conditional Probability: It is the probability of the happening of event A, when it is known that another event B has already happened and is denoted by P (A|B)i.e., P(A|B) = P(A ∩ B)/ P(B)Bayes theorem: It is referred to as the applications of the results of probability theory that involve estimating unknown probabilities and making decisions on the basis of new sample information. It is useful in solving business problems in the presence of additional information. The reason behind the popularity of this theorem is because of its usefulness in revising a set of old probabilities (Prior Probability) with some additional information and to derive a set of new probabilities (Posterior Probability).From the above equation it is inferred that “Bayes theorem explains the relationship between the Conditional Probabilities of events.” This theorem works mainly on uncertainty samples of data and is helpful in determining the ‘Specificity’ and ‘Sensitivity’ of data. This theorem plays an important role in drawing the CONFUSION MATRIX.Confusion matrix is a table-like structure that measures the performance of Machine Learning Models or Algorithms that we develop. This is helpful in determining the True Positive rates, True Negative Rates, False Positive Rates, False Negative Rates, Precision, Recall, F1-score, Accuracy, and Specificity in drawing the ROC Curve from the given data.We need to further focus on Probability distributions which are classified as Discrete and Continuous, Likelihood Estimation Functions etc. In Machine Learning, the Naive Bayes Algorithm works on the probabilistic way, with the assumption that input features are independent.Probability is an important area in most business applications as it helps in predicting the future outcomes from the data and takes further steps. Data Scientists, Data Analysts, and Machine Learning Engineers use this probability concept very often as their job is to take inputs and predict the possible outcomes.CALCULUS:This is a branch of Mathematics, that helps in studying rates of change of quantities. It deals with optimizing the performance of machine learning models or Algorithms. Without understanding this concept of calculus, it is difficult to compute probabilities on the data and we cannot draw the possible outcomes from the data we take. Calculus is mainly focused on integrals, limits, derivatives, and functions. It is divided into two types called Differential Statistics and Inferential Statistics. It is used in back propagation algorithms to train deep Neural Networks.Differential Calculus splits the given data into small pieces to know how it changes.Inferential Calculus combines (joins) the small pieces to find how much there is.Calculus is mainly used in optimizing Machine Learning and Deep Learning Algorithms. It is used to develop fast and efficient solutions. The concept of calculus is used in Algorithms like Gradient Descent and Stochastic Gradient Descent (SGD) algorithms and in Optimizers like Adam, Rms Drop, Adadelta etc.Data Scientists mainly use calculus in building many Deep Learning and Machine Learning Models. They are involved in optimizing the data and bringing out better outputs of data, by drawing intelligent insights hidden in them.Linear Algebra:Linear Algebra focuses more on computation. It plays a crucial role in understanding the background theory behind Machine learning and is also used for Deep Learning. It gives us better insights into how the algorithms really work in day-to-day life, and enables us to take better decisions. It mostly deals with Vectors and Matrices.A scalar is a single number.A vector is an array of numbers represented in a row or column, and it has only a single index for accessing it (i.e., either Rows or Columns)A matrix is a 2D array of numbers and can be accessed with the help of both the indices (i.e., by both rows and columns)A tensor is an array of numbers, placed in a grid in a particular order with a variable number of axes.The package named Numpy in the Python library is used in computation of all these numerical operations on the data. The Numpy library carries out the basic operations like addition, subtraction, Multiplication, division etc., of vectors and matrices and results in a meaningful value at the end. Numpy is represented in the form of N-d array.Machine learning models cannot be developed, complex data structures cannot be manipulated, and operations on matrices would not have been performed without the presence of Linear Algebra. All the results of the models are displayed using Linear Algebra as a platform.Some of the Machine Learning algorithms like Linear, Logistic regression, SVM and Decision trees use Linear Algebra in building the algorithms. And with the help of Linear Algebra we can build our own ML algorithms. Data Scientists and Machine Learning Engineers work with Linear Algebra in building their own algorithms when working with data.How do Python functions correlate to Mathematical Functions?So far, we have seen the importance of Mathematics in Machine Learning. But how do Mathematical functions corelate to Python functions when building a machine learning algorithm? The answer is quite simple. In Python, we take the data from our dataset and apply many functions to it. The data can be of different forms like characters, strings, numerical, float values, double values, Boolean values, special characters, Garbage values etc., in the data set that we take to solve a particular machine learning problem. But we commonly know that the computer understands only “zeroes & ones”. Whatever we take as input to our machine learning model from the dataset, the computer is going to understand it as binary “Zeroes & ones” only.Here the Python functions like “Numpy, Scipy, Pandas etc.,” mostly use pre-defined functions or libraries. These help us in applying the Mathematical functions to get better insights of the data from the dataset that we take. They help us to work on different types of data for processing and extracting information from them. Those functions further help us in cleaning the garbage values in data, the noise present in data and the null values present in data and finally help to make the dataset free from all the unwanted matter present in it. Once the data is preprocessed with the Python functions, we can apply our algorithms on the dataset to know which model works better for the data and we can find the accuracies of different algorithms applied on our dataset. The mathematical functions help us in visualizing the content present in the dataset, and helps to get better understanding on the data that we take and the problem we are addressing using a machine learning algorithm.Every algorithm that we use to build a machine learning model has math functions hidden in it, in the form of Python code. The algorithm that we develop can be used to solve a variety of things like a Boolean problem or a matrix problem like identifying an image in a crowd of people and much more. The final stage is to find the best algorithm that suits the model. This is where the mathematical functions in the Python language help us. It helps to analyze which algorithm is best through comparison functions like correlation, F1 score, Accuracy, Specificity, sensitivity etc. Mathematical functions also help us in finding out if the selected model is overfitting or underfitting to the data that we take.To conclude, we cannot apply the mathematical functions directly in building machine learning models, so we need a language to implement the mathematical strategies in the algorithm. This is why we use Python to implement our math models and draw better insights from the data. Python is a suitable language for implementations of this type. It is considered to be the best language among the other languages for solving real-world problems and implementing new techniques and strategies in the field of ML & Data Science.Conclusion:For machine learning enthusiasts and aspirants, mathematics is a crucial aspect to focus on, and it is important to build a strong foundation in Math. Each and every concept you learn in Machine Learning, every small algorithm you write or implement in solving a problem directly or indirectly has a relation to Mathematics.The concepts of math that are implemented in machine learning are built upon the basic math that we learn in 11th and 12th grades. It is the theoretical knowledge that we gain at that stage, but in the area of Machine Learning we experience the practical use cases of math that we have studied earlier.The best way to get familiar with the concepts of Mathematics is to take a Machine Learning Algorithm, find a use case, and solve and understand the math behind it.An understanding of math is paramount to enable us to come up with machine learning solutions to real world problems. A thorough knowledge of math concepts also helps us enhance our problem-solving skills.
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The Role of Mathematics in Machine Learning

Automation and machine learning have changed our l... Read More