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What is Bias-Variance Tradeoff in Machine Learning

What is Machine Learning? Machine Learning is a multidisciplinary field of study, which gives computers the ability to solve complex problems, which otherwise would be nearly impossible to be hand-coded by a human being. Machine Learning is a scientific field of study which involves the use of algorithms and statistics to perform a given task by relying on inference from data instead of explicit instructions. Machine Learning Process:The process of Machine Learning can be broken down into several parts, most of which is based around “Data”. The following steps show the Machine Learning Process. 1. Gathering Data from various sources: Since Machine Learning is basically the inference drawn from data before any algorithm can be used, data needs to be collected from some source. Data collected can be of any form, viz. Video data, Image data, Audio data, Text data, Statistical data, etc. 2. Cleaning data to have homogeneity: The data that is collected from various sources does not always come in the desired form. More importantly, data contains various irregularities like Missing data and Outliers.These irregularities may cause the Machine Learning Model(s) to perform poorly. Hence, the removal or processing of irregularities is necessary to promote data homogeneity. This step is also known as data pre-processing. 3. Model Building & Selecting the right Machine Learning Model: After the data has been correctly pre-processed, various Machine Learning Algorithms (or Models) are applied on the data to train the model to predict on unseen data, as well as to extract various insights from the data. After various models are “trained” to the data, the best performing model(s) that suit the application and the performance criteria are selected.4. Getting Insights from the model’s results: Once the model is selected, further data is used to validate the performance and accuracy of the model and get insights as to how the model performs under various conditions. 5. Data Visualization: This is the final step, where the model is used to predict unseen and real-world data. However, these predictions are not directly understandable to the user, and hence, data Visualization or converting the results into understandable visual graphs is necessary. At this stage, the model can be deployed to solve real-world problems.How is Machine Learning different from Curve Fitting? To get the similarities out of the way, both, Machine Learning and Curve Fitting rely on data to infer a model which, ideally, fits the data perfectly. The difference comes in the availability of the data. Curve Fitting is carried out with data, all of which is already available to the user. Hence, there is no question of the model to encounter unseen data.However, in Machine Learning, only a part of the data is available to the user at the time of training (fitting) the model, and then the model has to perform equally well on data that it has never encountered before. Which is, in other words, the generalization of the model over a given data, such that it is able to correctly predict when it is deployed.A high-level introduction to Bias and Variance through illustrative and applied examples Let’s initiate the idea of Bias and Variance with a case study. Let’s assume a simple dataset of predicting the price of a house based on its carpet area. Here, the x-axis represents the carpet area of the house, and the y-axis represents the price of the property. The plotted data (in a 2D graph) is shown in the graph below: The goal is to build a model to predict the price of the house, given the carpet area of the property. This is a rather easy problem to solve and can easily be achieved by fitting a curve to the given data points. But, for the time being, let’s concentrate on solving the same using Machine Learning.In order to keep this example simple and concentrate on Bias and Variance, a few assumptions are made:Adequate data is present in order to come up with a working model capable of making relatively accurate predictions.The data is homogeneous in nature and hence no major pre-processing steps are involved.There are no missing values or outliers, and hence they do not interfere with the outcome in any way. The y-axis data-points are independent of the order of the sequence of the x-axis data-points.With the above assumptions, the data is processed to train the model using the following steps: 1. Shuffling the data: Since the y-axis data-points are independent of the order of the sequence of the x-axis data-points, the dataset is shuffled in a pseudo-random manner. This is done to avoid unnecessary patterns from being learned by the model. During the shuffling, it is imperative to keep each x-y pair data point constant. Mixing them up will change the dataset itself and the model will learn inaccurate patterns. 2. Data Splitting: The dataset is split into three categories: Training Set (60%), Validation Set (20%), and Testing Set (20%). These three sets are used for different purposes:Training Set - This part of the dataset is used to train the model. It is also known as the Development Set. Validation Set - This is separate from the Training Set and is only used for model selection. The model does not train or learn from this part of the dataset.Testing Set - This part of the dataset is used for performance evaluation and is completely independent of the Training or Validation Sets. Similar to the Validation Set, the model does not train on this part of the dataset.3. Model Selection: Several Machine Learning Models are applied to the Training Set and their Training and Validation Losses are determined, which then helps determine the most appropriate model for the given dataset.During this step, we assume that a polynomial equation fits the data correctly. The general equation is given below: The process of “Training” mathematically is nothing more than figuring out the appropriate values for the parameters: a0, a1, ... ,an, which is done automatically by the model using the Training Set.The developer does have control over how high the degree of the polynomial can be. These parameters that can be tuned by the developer are called Hyperparameters. These hyperparameters play a key role in deciding how well would the model learn and how generalized will the learned parameters be. Given below are two graphs representing the prediction of the trained model on training data. The graph on the left represents a linear model with an error of 3.6, and the graph on the right represents a polynomial model with an error of 1.7. By looking at the errors, it can be concluded that the polynomial model performs significantly better when compared to the linear model (Lower the error, better is the performance of the model). However, when we use the same trained models on the Testing Set, the models perform very differently. The graph on the left represents the same linear model’s prediction on the Testing Set, and the graph on the right side represents the Polynomial model’s prediction on the Testing Set. It is clearly visible that the Polynomial model inaccurately predicts the outputs when compared to the Linear model.In terms of error, the total error for the Linear model is 3.6 and for the Polynomial model is a whopping 929.12. Such a big difference in errors between the Training and Testing Set clearly signifies that something is wrong with the Polynomial model. This drastic change in error is due to a phenomenon called Bias-Variance Tradeoff.What is “Error” in Machine Learning? Error in Machine Learning is the difference in the expected output and the predicted output of the model. It is a measure of how well the model performs over a given set of data.There are several methods to calculate error in Machine Learning. One of the most commonly used terminologies to represent the error is called the Loss/Cost Function. It is also known as the Mean Squared Error (or MSE) and is given by the following equation:The necessity of minimization of Errors: As it is obvious from the previously shown graphs, the higher the error, the worse the model performs. Hence, the error of the prediction of a model can be considered as a performance measure: Lower the error of a model, the better it performs. In addition to that, a model judges its own performance and trains itself based on the error created between its own output and the expected output. The primary target of the model is to minimize the error so as to get the best parameters that would fit the data perfectly. Total Error: The error mentioned above is the Total Error and consists of three types of errors: Bias + Variance + Irreducible Error. Total Error = Bias + Variance + Irreducible ErrorEven for an ideal model, it is impossible to get rid of all the types of errors. The “irreducible” error rate is caused by the presence of noise in the data and hence is not removable. However, the Bias and Variance errors can be reduced to a minimum and hence, the total error can also be reduced significantly. Why is the splitting of data important? Ideally, the complete dataset is not used to train the model. The dataset is split into three sets: Training, Validation and Testing Sets. Each of these serves a specific role in the development of a model which performs well under most conditions.Training Set (60-80%): The largest portion of the dataset is used for training the Machine Learning Model. The model extracts the features and learns to recognize the patterns in the dataset. The quality and quantity of the training set determines how well the model is going to perform. Testing Set (15-25%): The main goal of every Machine Learning Engineer is to develop a model which would generalize the best over a given dataset. This is achieved by training the model(s) on a portion of the dataset and testing its performance by applying the trained model on another portion of the same/similar dataset that has not been used during training (Testing Set). This is important since the model might perform too well on the training set, but perform poorly on unseen data, as was the case with the example given above. Testing set is primarily used for model performance evaluation.Validation Set (15-25%): In addition to the above, because of the presence of more than one Machine Learning Algorithm (model), it is often not recommended to test the performance of multiple models on the same dataset and then choose the best one. This process is called Model Selection, and for this, a separate part of the training set is used, which is also known as Validation Set. A validation set behaves similar to a testing set but is primarily used in model selection and not in performance evaluation.Bias and Variance - A Technical Introduction What is Bias?Bias is used to allow the Machine Learning Model to learn in a simplified manner. Ideally, the simplest model that is able to learn the entire dataset and predict correctly on it is the best model. Hence, bias is introduced into the model in the view of achieving the simplest model possible.Parameter based learning algorithms usually have high bias and hence are faster to train and easier to understand. However, too much bias causes the model to be oversimplified and hence underfits the data. Hence these models are less flexible and often fail when they are applied on complex problems.Mathematically, it is the difference between the model’s average prediction and the expected value.What is Variance?Variance in data is the variability of the model in a case where different Training Data is used. This would significantly change the estimation of the target function. Statistically, for a given random variable, Variance is the expectation of squared deviation from its mean. In other words, the higher the variance of the model, the more complex the model is and it is able to learn more complex functions. However, if the model is too complex for the given dataset, where a simpler solution is possible, a model with high Variance causes the model to overfit. When the model performs well on the Training Set and fails to perform on the Testing Set, the model is said to have Variance.Characteristics of a biased model A biased model will have the following characteristics:Underfitting: A model with high bias is simpler than it should be and hence tends to underfit the data. In other words, the model fails to learn and acquire the intricate patterns of the dataset. Low Training Accuracy: A biased model will not fit the Training Dataset properly and hence will have low training accuracy (or high training loss). Inability to solve complex problems: A Biased model is too simple and hence is often incapable of learning complex features and solving relatively complex problems.Characteristics of a model with Variance A model with high Variance will have the following characteristics:Overfitting: A model with high Variance will have a tendency to be overly complex. This causes the overfitting of the model.Low Testing Accuracy: A model with high Variance will have very high training accuracy (or very low training loss), but it will have a low testing accuracy (or a low testing loss). Overcomplicating simpler problems: A model with high variance tends to be overly complex and ends up fitting a much more complex curve to a relatively simpler data. The model is thus capable of solving complex problems but incapable of solving simple problems efficiently.What is Bias-Variance Tradeoff? From the understanding of bias and variance individually thus far, it can be concluded that the two are complementary to each other. In other words, if the bias of a model is decreased, the variance of the model automatically increases. The vice-versa is also true, that is if the variance of a model decreases, bias starts to increase.Hence, it can be concluded that it is nearly impossible to have a model with no bias or no variance since decreasing one increases the other. This phenomenon is known as the Bias-Variance TradeA graphical introduction to Bias-Variance Tradeoff In order to get a clear idea about the Bias-Variance Tradeoff, let us consider the bulls-eye diagram. Here, the central red portion of the target can be considered the location where the model correctly predicts the values. As we move away from the central red circle, the error in the prediction starts to increase. Each of the several hits on the target is achieved by repetition of the model building process. Each hit represents the individual realization of the model. As can be seen in the diagram below, the bias and the variance together influence the predictions of the model under different circumstances.Another way of looking at the Bias-Variance Tradeoff graphically is to plot the graphical representation for error, bias, and variance versus the complexity of the model. In the graph shown below, the green dotted line represents variance, the blue dotted line represents bias and the red solid line represents the error in the prediction of the concerned model. Since bias is high for a simpler model and decreases with an increase in model complexity, the line representing bias exponentially decreases as the model complexity increases. Similarly, Variance is high for a more complex model and is low for simpler models. Hence, the line representing variance increases exponentially as the model complexity increases. Finally, it can be seen that on either side, the generalization error is quite high. Both high bias and high variance lead to a higher error rate. The most optimal complexity of the model is right in the middle, where the bias and variance intersect. This part of the graph is shown to produce the least error and is preferred. Also, as discussed earlier, the model underfits for high-bias situations and overfits for high-variance situations.Mathematical Expression of Bias-Variance Tradeoff The expected values is a vector represented by y. The predicted output of the model is denoted by the vector y for input vector x. The relationship between the predicted values and the inputs can be taken as y = f(x) + e, where e is the normally distributed error given by:The third term in the above equation, irreducible_error represents the noise term and cannot be fundamentally reduced by any given model. If hypothetically, infinite data is available, it is possible to tune the model to reduce the bias and variance terms to zero but is not possible to do so practically. Hence, there is always a tradeoff between the minimization of bias and variance. Detection of Bias and Variance of a modelIn model building, it is imperative to have the knowledge to detect if the model is suffering from high bias or high variance. The methods to detect high bias and variance is given below:Detection of High Bias:The model suffers from a very High Training Error.The Validation error is similar in magnitude to the training error.The model is underfitting.Detection of High Variance:The model suffers from a very Low Training Error.The Validation error is very high when compared to the training error.The model is overfitting.A graphical method to Detect a model suffering from High Bias and Variance is shown below: The graph shows the change in error rate with respect to model complexity for training and validation error. The left portion of the graph suffers from High Bias. This can be seen as the training error is quite high along with the validation error. In addition to that, model complexity is quite low. The right portion of the graph suffers from High Variance. This can be seen as the training error is very low, yet the validation error is very high and starts increasing with increasing model complexity.A systematic approach to solve a Bias-Variance Problem by Dr. Andrew Ng:Dr. Andrew Ng proposed a very simple-to-follow step by step architecture to detect and solve a High Bias and High Variance errors in a model. The block diagram is shown below:Detection and Solution to High Bias problem - if the training error is high: Train longer: High bias means a usually less complex model, and hence it requires more training iterations to learn the relevant patterns. Hence, longer training solves the error sometimes.Train a more complex model: As mentioned above, high bias is a result of a less than optimal complexity in the model. Hence, to avoid high bias, the existing model can be swapped out with a more complex model. Obtain more features: It is often possible that the existing dataset lacks the required essential features for effective pattern recognition. To remedy this problem: More features can be collected for the existing data.Feature Engineering can be performed on existing features to extract more non-linear features. Decrease regularization: Regularization is a process to decrease model complexity by regularizing the inputs at different stages, promote generalization and prevent overfitting in the process. Decreasing regularization allows the model to learn the training dataset better. New model architecture: If all of the above-mentioned methods fail to deliver satisfactory results, then it is suggested to try out other new model architectures. Detection and Solution to High Variance problem - if a validation error is high: Obtain more data: High variance is often caused due to a lack of training data. The model complexity and quantity of training data need to be balanced. A model of higher complexity requires a larger quantity of training data. Hence, if the model is suffering from high variance, more datasets can reduce the variance. Decrease number of features: If the dataset consists of too many features for each data-point, the model often starts to suffer from high variance and starts to overfit. Hence, decreasing the number of features is recommended. Increase Regularization: As mentioned above, regularization is a process to decrease model complexity. Hence, if the model is suffering from high variance (which is caused by a complex model), then an increase in regularization can decrease the complexity and help to generalize the model better.New model architecture: Similar to the solution of a model suffering from high bias, if all of the above-mentioned methods fail to deliver satisfactory results, then it is suggested to try out other new model architectures.Conclusion To summarize, Bias and Variance play a major role in the training process of a model. It is necessary to reduce each of these parameters individually to the minimum possible value. However, it should be kept in mind that an effort to decrease one of these parameters beyond a certain limit increases the probability of the other getting increased. This phenomenon is called as the Bias-Variance Tradeoff and is a parameter to consider during model building. 

What is Bias-Variance Tradeoff in Machine Learning

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  • by Animikh Aich
  • 25th Jul, 2019
  • Last updated on 11th Mar, 2021
  • 24 mins read
What is Bias-Variance Tradeoff in Machine Learning

What is Machine Learning? 

Machine Learning is a multidisciplinary field of study, which gives computers the ability to solve complex problems, which otherwise would be nearly impossible to be hand-coded by a human being. Machine Learning is a scientific field of study which involves the use of algorithms and statistics to perform a given task by relying on inference from data instead of explicit instructions. 

Machine Learning Process:

Machine Learning Process

The process of Machine Learning can be broken down into several parts, most of which is based around “Data”. The following steps show the Machine Learning Process. 

1. Gathering Data from various sources: Since Machine Learning is basically the inference drawn from data before any algorithm can be used, data needs to be collected from some source. Data collected can be of any form, viz. Video data, Image data, Audio data, Text data, Statistical data, etc.

2. Cleaning data to have homogeneity: The data that is collected from various sources does not always come in the desired form. More importantly, data contains various irregularities like 
Missing data and Outliers.These irregularities may cause the Machine Learning Model(s) to perform poorly. Hence, the removal or processing of irregularities is necessary to promote data homogeneity. This step is also known as data pre-processing.

3. Model Building & Selecting the right Machine Learning Model: After the data has been correctly pre-processed, various Machine Learning Algorithms (or Models) are applied on the data to train the model to predict on unseen data, as well as to extract various insights from the data. After various models are “trained” to the data, the best performing model(s) that suit the application and the performance criteria are selected.

4. Getting Insights from the model’s results: Once the model is selected, further data is used to validate the performance and accuracy of the model and get insights as to how the model performs under various conditions.

5. Data Visualization: This is the final step, where the model is used to predict unseen and real-world data. However, these predictions are not directly understandable to the user, and hence, data Visualization or converting the results into understandable visual graphs is necessary. At this stage, the model can be deployed to solve real-world problems.

How is Machine Learning different from Curve Fitting? 

To get the similarities out of the way, both, Machine Learning and Curve Fitting rely on data to infer a model which, ideally, fits the data perfectly. 

The difference comes in the availability of the data. 

  • Curve Fitting is carried out with data, all of which is already available to the user. Hence, there is no question of the model to encounter unseen data.
  • However, in Machine Learning, only a part of the data is available to the user at the time of training (fitting) the model, and then the model has to perform equally well on data that it has never encountered before. Which is, in other words, the generalization of the model over a given data, such that it is able to correctly predict when it is deployed.

A high-level introduction to Bias and Variance through illustrative and applied examples 

Let’s initiate the idea of Bias and Variance with a case study. Let’s assume a simple dataset of predicting the price of a house based on its carpet area. Here, the x-axis represents the carpet area of the house, and the y-axis represents the price of the property. The plotted data (in a 2D graph) is shown in the graph below: 

Examples of high level introduction to Bias and Variance

The goal is to build a model to predict the price of the house, given the carpet area of the property. This is a rather easy problem to solve and can easily be achieved by fitting a curve to the given data points. But, for the time being, let’s concentrate on solving the same using Machine Learning.

In order to keep this example simple and concentrate on Bias and Variance, a few assumptions are made:

  • Adequate data is present in order to come up with a working model capable of making relatively accurate predictions.
  • The data is homogeneous in nature and hence no major pre-processing steps are involved.
  • There are no missing values or outliers, and hence they do not interfere with the outcome in any way. 
  • The y-axis data-points are independent of the order of the sequence of the x-axis data-points.

With the above assumptions, the data is processed to train the model using the following steps: 

1. Shuffling the data: Since the y-axis data-points are independent of the order of the sequence of the x-axis data-points, the dataset is shuffled in a pseudo-random manner. This is done to avoid unnecessary patterns from being learned by the model. During the shuffling, it is imperative to keep each x-y pair data point constant. Mixing them up will change the dataset itself and the model will learn inaccurate patterns. 

2. Data Splitting: The dataset is split into three categories: Training Set (60%), Validation Set (20%), and Testing Set (20%). These three sets are used for different purposes:

  • Training Set - This part of the dataset is used to train the model. It is also known as the Development Set. 
  • Validation Set - This is separate from the Training Set and is only used for model selection. The model does not train or learn from this part of the dataset.
  • Testing Set - This part of the dataset is used for performance evaluation and is completely independent of the Training or Validation Sets. Similar to the Validation Set, the model does not train on this part of the dataset.

3. Model Selection: Several Machine Learning Models are applied to the Training Set and their Training and Validation Losses are determined, which then helps determine the most appropriate model for the given dataset.
During this step, we assume that a polynomial equation fits the data correctly. The general equation is given below: 

Model Selection Equation

The process of “Training” mathematically is nothing more than figuring out the appropriate values for the parameters: a0, a1, ... ,an, which is done automatically by the model using the Training Set.

The developer does have control over how high the degree of the polynomial can be. These parameters that can be tuned by the developer are called Hyperparameters. These hyperparameters play a key role in deciding how well would the model learn and how generalized will the learned parameters be. 

Given below are two graphs representing the prediction of the trained model on training data. The graph on the left represents a linear model with an error of 3.6, and the graph on the right represents a polynomial model with an error of 1.7. 

Model Selection Graph of Machine Learning:- Linear Model Error and Polynomial Model Error

By looking at the errors, it can be concluded that the polynomial model performs significantly better when compared to the linear model (Lower the error, better is the performance of the model). 

However, when we use the same trained models on the Testing Set, the models perform very differently. The graph on the left represents the same linear model’s prediction on the Testing Set, and the graph on the right side represents the Polynomial model’s prediction on the Testing Set. It is clearly visible that the Polynomial model inaccurately predicts the outputs when compared to the Linear model.

Model Selection Graph of machine Learning with Testing set:- Linear and Polynomial model Error

In terms of error, the total error for the Linear model is 3.6 and for the Polynomial model is a whopping 929.12. 

Such a big difference in errors between the Training and Testing Set clearly signifies that something is wrong with the Polynomial model. This drastic change in error is due to a phenomenon called Bias-Variance Tradeoff.

What is “Error” in Machine Learning? 

Error in Machine Learning is the difference in the expected output and the predicted output of the model. It is a measure of how well the model performs over a given set of data.

There are several methods to calculate error in Machine Learning. One of the most commonly used terminologies to represent the error is called the Loss/Cost Function. It is also known as the Mean Squared Error (or MSE) and is given by the following equation:

Mean Squared Error formula definition

The necessity of minimization of Errors: As it is obvious from the previously shown graphs, the higher the error, the worse the model performs. Hence, the error of the prediction of a model can be considered as a performance measure: Lower the error of a model, the better it performs. 

In addition to that, a model judges its own performance and trains itself based on the error created between its own output and the expected output. The primary target of the model is to minimize the error so as to get the best parameters that would fit the data perfectly. 

Total Error: The error mentioned above is the Total Error and consists of three types of errors: Bias + Variance + Irreducible Error. 

Total Error = Bias + Variance + Irreducible Error

Even for an ideal model, it is impossible to get rid of all the types of errors. The “irreducible” error rate is caused by the presence of noise in the data and hence is not removable. However, the Bias and Variance errors can be reduced to a minimum and hence, the total error can also be reduced significantly. 

Why is the splitting of data important? 

Ideally, the complete dataset is not used to train the model. The dataset is split into three sets: Training, Validation and Testing Sets. Each of these serves a specific role in the development of a model which performs well under most conditions.

Training Set (60-80%): The largest portion of the dataset is used for training the Machine Learning Model. The model extracts the features and learns to recognize the patterns in the dataset. The quality and quantity of the training set determines how well the model is going to perform. 

Testing Set (15-25%): The main goal of every Machine Learning Engineer is to develop a model which would generalize the best over a given dataset. This is achieved by training the model(s) on a portion of the dataset and testing its performance by applying the trained model on another portion of the same/similar dataset that has not been used during training (Testing Set). This is important since the model might perform too well on the training set, but perform poorly on unseen data, as was the case with the example given above. Testing set is primarily used for model performance evaluation.

Validation Set (15-25%): In addition to the above, because of the presence of more than one Machine Learning Algorithm (model), it is often not recommended to test the performance of multiple models on the same dataset and then choose the best one. This process is called Model Selection, and for this, a separate part of the training set is used, which is also known as Validation Set. A validation set behaves similar to a testing set but is primarily used in model selection and not in performance evaluation.

Bias and Variance - A Technical Introduction 

What is Bias?

Bias is used to allow the Machine Learning Model to learn in a simplified manner. Ideally, the simplest model that is able to learn the entire dataset and predict correctly on it is the best model. Hence, bias is introduced into the model in the view of achieving the simplest model possible.

Parameter based learning algorithms usually have high bias and hence are faster to train and easier to understand. However, too much bias causes the model to be oversimplified and hence underfits the data. Hence these models are less flexible and often fail when they are applied on complex problems.

Mathematically, it is the difference between the model’s average prediction and the expected value.

What is Variance?

Variance in data is the variability of the model in a case where different Training Data is used. This would significantly change the estimation of the target function. Statistically, for a given random variable, Variance is the expectation of squared deviation from its mean. 

In other words, the higher the variance of the model, the more complex the model is and it is able to learn more complex functions. However, if the model is too complex for the given dataset, where a simpler solution is possible, a model with high Variance causes the model to overfit. 

When the model performs well on the Training Set and fails to perform on the Testing Set, the model is said to have Variance.

Characteristics of a biased model 

A biased model will have the following characteristics:

  • Underfitting: A model with high bias is simpler than it should be and hence tends to underfit the data. In other words, the model fails to learn and acquire the intricate patterns of the dataset. 
  • Low Training Accuracy: A biased model will not fit the Training Dataset properly and hence will have low training accuracy (or high training loss). 
  • Inability to solve complex problems: A Biased model is too simple and hence is often incapable of learning complex features and solving relatively complex problems.

Characteristics of a model with Variance 

A model with high Variance will have the following characteristics:

  • Overfitting: A model with high Variance will have a tendency to be overly complex. This causes the overfitting of the model.
  • Low Testing Accuracy: A model with high Variance will have very high training accuracy (or very low training loss), but it will have a low testing accuracy (or a low testing loss). 
  • Overcomplicating simpler problems: A model with high variance tends to be overly complex and ends up fitting a much more complex curve to a relatively simpler data. The model is thus capable of solving complex problems but incapable of solving simple problems efficiently.

What is Bias-Variance Tradeoff? 

Bias-variance Tradeoff Graph

From the understanding of bias and variance individually thus far, it can be concluded that the two are complementary to each other. In other words, if the bias of a model is decreased, the variance of the model automatically increases. The vice-versa is also true, that is if the variance of a model decreases, bias starts to increase.

Hence, it can be concluded that it is nearly impossible to have a model with no bias or no variance since decreasing one increases the other. This phenomenon is known as the Bias-Variance Trade

A graphical introduction to Bias-Variance Tradeoff 

In order to get a clear idea about the Bias-Variance Tradeoff, let us consider the bulls-eye diagram. Here, the central red portion of the target can be considered the location where the model correctly predicts the values. As we move away from the central red circle, the error in the prediction starts to increase. 

Each of the several hits on the target is achieved by repetition of the model building process. Each hit represents the individual realization of the model. As can be seen in the diagram below, the bias and the variance together influence the predictions of the model under different circumstances.

A graphical introduction to Bias-Variance Tradeoff


Another way of looking at the Bias-Variance Tradeoff graphically is to plot the graphical representation for error, bias, and variance versus the complexity of the model. In the graph shown below, the green dotted line represents variance, the blue dotted line represents bias and the red solid line represents the error in the prediction of the concerned model. 

  • Since bias is high for a simpler model and decreases with an increase in model complexity, the line representing bias exponentially decreases as the model complexity increases. 
  • Similarly, Variance is high for a more complex model and is low for simpler models. Hence, the line representing variance increases exponentially as the model complexity increases. 
  • Finally, it can be seen that on either side, the generalization error is quite high. Both high bias and high variance lead to a higher error rate. 
  • The most optimal complexity of the model is right in the middle, where the bias and variance intersect. This part of the graph is shown to produce the least error and is preferred. 
  • Also, as discussed earlier, the model underfits for high-bias situations and overfits for high-variance situations.

 Bias-Variance Tradeoff graph with 2D plots

Mathematical Expression of Bias-Variance Tradeoff 

The expected values is a vector represented by y. The predicted output of the model is denoted by the vector y for input vector x. The relationship between the predicted values and the inputs can be taken as y = f(x) + e, where e is the normally distributed error given by:

Mathematical Expression for Error in Bias-Variance tradeoff

The third term in the above equation, irreducible_error represents the noise term and cannot be fundamentally reduced by any given model. If hypothetically, infinite data is available, it is possible to tune the model to reduce the bias and variance terms to zero but is not possible to do so practically. Hence, there is always a tradeoff between the minimization of bias and variance. 

Detection of Bias and Variance of a model

In model building, it is imperative to have the knowledge to detect if the model is suffering from high bias or high variance. The methods to detect high bias and variance is given below:

  1. Detection of High Bias:
    • The model suffers from a very High Training Error.
    • The Validation error is similar in magnitude to the training error.
    • The model is underfitting.
  2. Detection of High Variance:
    • The model suffers from a very Low Training Error.
    • The Validation error is very high when compared to the training error.
    • The model is overfitting.

A graphical method to Detect a model suffering from High Bias and Variance is shown below: 

Model suffering from High Bias and Variance

The graph shows the change in error rate with respect to model complexity for training and validation error. 

  • The left portion of the graph suffers from High Bias. This can be seen as the training error is quite high along with the validation error. In addition to that, model complexity is quite low. 
  • The right portion of the graph suffers from High Variance. This can be seen as the training error is very low, yet the validation error is very high and starts increasing with increasing model complexity.

A systematic approach to solve a Bias-Variance Problem by Dr. Andrew Ng:

Dr. Andrew Ng proposed a very simple-to-follow step by step architecture to detect and solve a High Bias and High Variance errors in a model. The block diagram is shown below:

Flow Chart to solve High Bias and High Variance Errors in a model

Detection and Solution to High Bias problem - if the training error is high: 

  1. Train longer: High bias means a usually less complex model, and hence it requires more training iterations to learn the relevant patterns. Hence, longer training solves the error sometimes.
  2. Train a more complex model: As mentioned above, high bias is a result of a less than optimal complexity in the model. Hence, to avoid high bias, the existing model can be swapped out with a more complex model. 
  3. Obtain more features: It is often possible that the existing dataset lacks the required essential features for effective pattern recognition. To remedy this problem: 
    • More features can be collected for the existing data.
    • Feature Engineering can be performed on existing features to extract more non-linear features. 
  4. Decrease regularization: Regularization is a process to decrease model complexity by regularizing the inputs at different stages, promote generalization and prevent overfitting in the process. Decreasing regularization allows the model to learn the training dataset better. 
  5. New model architecture: If all of the above-mentioned methods fail to deliver satisfactory results, then it is suggested to try out other new model architectures. 

Detection and Solution to High Variance problem - if a validation error is high: 

  1. Obtain more data: High variance is often caused due to a lack of training data. The model complexity and quantity of training data need to be balanced. A model of higher complexity requires a larger quantity of training data. Hence, if the model is suffering from high variance, more datasets can reduce the variance. 
  2. Decrease number of features: If the dataset consists of too many features for each data-point, the model often starts to suffer from high variance and starts to overfit. Hence, decreasing the number of features is recommended. 
  3. Increase Regularization: As mentioned above, regularization is a process to decrease model complexity. Hence, if the model is suffering from high variance (which is caused by a complex model), then an increase in regularization can decrease the complexity and help to generalize the model better.
  4. New model architecture: Similar to the solution of a model suffering from high bias, if all of the above-mentioned methods fail to deliver satisfactory results, then it is suggested to try out other new model architectures.

Conclusion 

To summarize, Bias and Variance play a major role in the training process of a model. It is necessary to reduce each of these parameters individually to the minimum possible value. However, it should be kept in mind that an effort to decrease one of these parameters beyond a certain limit increases the probability of the other getting increased. This phenomenon is called as the Bias-Variance Tradeoff and is a parameter to consider during model building. 

Animikh

Animikh Aich

Computer Vision Engineer

Animikh Aich is a Deep Learning enthusiast, currently working as a Computer Vision Engineer. His work includes three International Conference publications and several projects based on Computer Vision and Machine Learning.

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1 comments

satish p 05 Aug 2019

I am interested in this blog, wish to get more information about the machine learning

Knowledgehut Editor 06 Aug 2019

Thanks for reaching out! You can find more on the machine learning at https://www.knowledgehut.com/blog/data-science/what-is-machine-learning

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Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course https://www.knowledgehut.com/data-science-courses. 
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Role of Unstructured Data in Data Science

Data has become the new game changer for businesses. Typically, data scientists categorize data into three broad divisions - structured, semi-structured, and unstructured data. In this article, you will get to know about unstructured data, sources of unstructured data, unstructured data vs. structured data, the use of structured and unstructured data in machine learning, and the difference between structured and unstructured data. Let us first understand what is unstructured data with examples. What is unstructured data? Unstructured data is a kind of data format where there is no organized form or type of data. Videos, texts, images, document files, audio materials, email contents and more are considered to be unstructured data. It is the most copious form of business data, and cannot be stored in a structured database or relational database. Some examples of unstructured data are the photos we post on social media platforms, the tagging we do, the multimedia files we upload, and the documents we share. Seagate predicts that the global data-sphere will expand to 163 zettabytes by 2025, where most of the data will be in the unstructured format. Characteristics of Unstructured DataUnstructured data cannot be organized in a predefined fashion, and is not a homogenous data model. This makes it difficult to manage. Apart from that, these are the other characteristics of unstructured data. You cannot store unstructured data in the form of rows and columns as we do in a database table. Unstructured data is heterogeneous in structure and does not have any specific data model. The creation of such data does not follow any semantics or habits. Due to the lack of any particular sequence or format, it is difficult to manage. Such data does not have an identifiable structure. Sources of Unstructured Data There are various sources of unstructured data. Some of them are: Content websites Social networking sites Online images Memos Reports and research papers Documents, spreadsheets, and presentations Audio mining, chatbots Surveys Feedback systems Advantages of Unstructured Data Unstructured data has become exceptionally easy to store because of MongoDB, Cassandra, or even using JSON. Modern NoSQL databases and software allows data engineers to collect and extract data from various sources. There are numerous benefits that enterprises and businesses can gain from unstructured data. These are: With the advent of unstructured data, we can store data that lacks a proper format or structure. There is no fixed schema or data structure for storing such data, which gives flexibility in storing data of different genres. Unstructured data is much more portable by nature. Unstructured data is scalable and flexible to store. Database systems like MongoDB, Cassandra, etc., can easily handle the heterogeneous properties of unstructured data. Different applications and platforms produce unstructured data that becomes useful in business intelligence, unstructured data analytics, and various other fields. Unstructured data analysis allows finding comprehensive data stories from data like email contents, website information, social media posts, mobile data, cache files and more. Unstructured data, along with data analytics, helps companies improve customer experience. Detection of the taste of consumers and their choices becomes easy because of unstructured data analysis. Disadvantages of Unstructured data Storing and managing unstructured data is difficult because there is no proper structure or schema. Data indexing is also a substantial challenge and hence becomes unclear due to its disorganized nature. Search results from an unstructured dataset are also not accurate because it does not have predefined attributes. Data security is also a challenge due to the heterogeneous form of data. Problems faced and solutions for storing unstructured data. Until recently, it was challenging to store, evaluate, and manage unstructured data. But with the advent of modern data analysis tools, algorithms, CAS (content addressable storage system), and big data technologies, storage and evaluation became easy. Let us first take a look at the various challenges used for storing unstructured data. Storing unstructured data requires a large amount of space. Indexing of unstructured data is a hectic task. Database operations such as deleting and updating become difficult because of the disorganized nature of the data. Storing and managing video, audio, image file, emails, social media data is also challenging. Unstructured data increases the storage cost. For solving such issues, there are some particular approaches. These are: CAS system helps in storing unstructured data efficiently. We can preserve unstructured data in XML format. Developers can store unstructured data in an RDBMS system supporting BLOB. We can convert unstructured data into flexible formats so that evaluating and storage becomes easy. Let us now understand the differences between unstructured data vs. structured data. Unstructured Data Vs. Structured Data In this section, we will understand the difference between structured and unstructured data with examples. STRUCTUREDUNSTRUCTUREDStructured data resides in an organized format in a typical database.Unstructured data cannot reside in an organized format, and hence we cannot store it in a typical database.We can store structured data in SQL database tables having rows and columns.Storing and managing unstructured data requires specialized databases, along with a variety of business intelligence and analytics applications.It is tough to scale a database schema.It is highly scalable.Structured data gets generated in colleges, universities, banks, companies where people have to deal with names, date of birth, salary, marks and so on.We generate or find unstructured data in social media platforms, emails, analyzed data for business intelligence, call centers, chatbots and so on.Queries in structured data allow complex joining.Unstructured data allows only textual queries.The schema of a structured dataset is less flexible and dependent.An unstructured dataset is flexible but does not have any particular schema.It has various concurrency techniques.It has no concurrency techniques.We can use SQL, MySQL, SQLite, Oracle DB, Teradata to store structured data.We can use NoSQL (Not Only SQL) to store unstructured data.Types of Unstructured Data Do you have any idea just how much of unstructured data we produce and from what sources? Unstructured data includes all those forms of data that we cannot actively manage in an RDBMS system that is a transactional system. We can store structured data in the form of records. But this is not the case with unstructured data. Before the advent of object-based storage, most of the unstructured data was stored in file-based systems. Here are some of the types of unstructured data. Rich media content: Entertainment files, surveillance data, multimedia email attachments, geospatial data, audio files (call center and other recorded audio), weather reports (graphical), etc., comes under this genre. Document data: Invoices, text-file records, email contents, productivity applications, etc., are included under this genre. Internet of Things (IoT) data: Ticker data, sensor data, data from other IoT devices come under this genre. Apart from all these, data from business intelligence and analysis, machine learning datasets, and artificial intelligence data training datasets are also a separate genre of unstructured data. Examples of Unstructured Data There are various sources from where we can obtain unstructured data. The prominent use of this data is in unstructured data analytics. Let us now understand what are some examples of unstructured data and their sources – Healthcare industries generate a massive volume of human as well as machine-generated unstructured data. Human-generated unstructured data could be in the form of patient-doctor or patient-nurse conversations, which are usually recorded in audio or text formats. Unstructured data generated by machines includes emergency video camera footage, surgical robots, data accumulated from medical imaging devices like endoscopes, laparoscopes and more.  Social Media is an intrinsic entity of our daily life. Billions of people come together to join channels, share different thoughts, and exchange information with their loved ones. They create and share such data over social media platforms in the form of images, video clips, audio messages, tagging people (this helps companies to map relations between two or more people), entertainment data, educational data, geolocations, texts, etc. Other spectra of data generated from social media platforms are behavior patterns, perceptions, influencers, trends, news, and events. Business and corporate documents generate a multitude of unstructured data such as emails, presentations, reports containing texts, images, presentation reports, video contents, feedback and much more. These documents help to create knowledge repositories within an organization to make better implicit operations. Live chat, video conferencing, web meeting, chatbot-customer messages, surveillance data are other prominent examples of unstructured data that companies can cultivate to get more insights into the details of a person. Some prominent examples of unstructured data used in enterprises and organizations are: Reports and documents, like Word files or PDF files Multimedia files, such as audio, images, designed texts, themes, and videos System logs Medical images Flat files Scanned documents (which are images that hold numbers and text – for example, OCR) Biometric data Unstructured Data Analytics Tools  You might be wondering what tools can come into use to gather and analyze information that does not have a predefined structure or model. Various tools and programming languages use structured and unstructured data for machine learning and data analysis. These are: Tableau MonkeyLearn Apache Spark SAS Python MS. Excel RapidMiner KNIME QlikView Python programming R programming Many cloud services (like Amazon AWS, Microsoft Azure, IBM Cloud, Google Cloud) also offer unstructured data analysis solutions bundled with their services. How to analyze unstructured data? In the past, the process of storage and analysis of unstructured data was not well defined. Enterprises used to carry out this kind of analysis manually. But with the advent of modern tools and programming languages, most of the unstructured data analysis methods became highly advanced. AI-powered tools use algorithms designed precisely to help to break down unstructured data for analysis. Unstructured data analytics tools, along with Natural language processing (NLP) and machine learning algorithms, help advanced software tools analyze and extract analytical data from the unstructured datasets. Before using these tools for analyzing unstructured data, you must properly go through a few steps and keep these points in mind. Set a clear goal for analyzing the data: It is essential to clear your intention about what insights you want to extract from your unstructured data. Knowing this will help you distinguish what type of data you are planning to accumulate. Collect relevant data: Unstructured data is available everywhere, whether it's a social media platform, online feedback or reviews, or a survey form. Depending on the previous point, that is your goal - you have to be precise about what data you want to collect in real-time. Also, keep in mind whether your collected details are relevant or not. Clean your data: Data cleaning or data cleansing is a significant process to detect corrupt or irrelevant data from the dataset, followed by modifying or deleting the coarse and sloppy data. This phase is also known as the data-preprocessing phase, where you have to reduce the noise, carry out data slicing for meaningful representation, and remove unnecessary data. Use Technology and tools: Once you perform the data cleaning, it is time to utilize unstructured data analysis tools to prepare and cultivate the insights from your data. Technologies used for unstructured data storage (NoSQL) can help in managing your flow of data. Other tools and programming libraries like Tableau, Matplotlib, Pandas, and Google Data Studio allows us to extract and visualize unstructured data. Data can be visualized and presented in the form of compelling graphs, plots, and charts. How to Extract information from Unstructured Data? With the growth in digitization during the information era, repetitious transactions in data cause data flooding. The exponential accretion in the speed of digital data creation has brought a whole new domain of understanding user interaction with the online world. According to Gartner, 80% of the data created by an organization or its application is unstructured. While extracting exact information through appropriate analysis of organized data is not yet possible, even obtaining a decent sense of this unstructured data is quite tough. Until now, there are no perfect tools to analyze unstructured data. But algorithms and tools designed using machine learning, Natural language processing, Deep learning, and Graph Analysis (a mathematical method for estimating graph structures) help us to get the upper hand in extracting information from unstructured data. Other neural network models like modern linguistic models follow unsupervised learning techniques to gain a good 'knowledge' about the unstructured dataset before going into a specific supervised learning step. AI-based algorithms and technologies are capable enough to extract keywords, locations, phone numbers, analyze image meaning (through digital image processing). We can then understand what to evaluate and identify information that is essential to your business. ConclusionUnstructured data is found abundantly from sources like documents, records, emails, social media posts, feedbacks, call-records, log-in session data, video, audio, and images. Manually analyzing unstructured data is very time-consuming and can be very boring at the same time. With the growth of data science and machine learning algorithms and models, it has become easy to gather and analyze insights from unstructured information.  According to some research, data analytics tools like MonkeyLearn Studio, Tableau, RapidMiner help analyze unstructured data 1200x faster than the manual approach. Analyzing such data will help you learn more about your customers as well as competitors. Text analysis software, along with machine learning models, will help you dig deep into such datasets and make you gain an in-depth understanding of the overall scenario with fine-grained analyses.
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Role of Unstructured Data in Data Science

Data has become the new game changer for busines... Read More

What Is Statistical Analysis and Its Business Applications?

Statistics is a science concerned with collection, analysis, interpretation, and presentation of data. In Statistics, we generally want to study a population. You may consider a population as a collection of things, persons, or objects under experiment or study. It is usually not possible to gain access to all of the information from the entire population due to logistical reasons. So, when we want to study a population, we generally select a sample. In sampling, we select a portion (or subset) of the larger population and then study the portion (or the sample) to learn about the population. Data is the result of sampling from a population.Major ClassificationThere are two basic branches of Statistics – Descriptive and Inferential statistics. Let us understand the two branches in brief. Descriptive statistics Descriptive statistics involves organizing and summarizing the data for better and easier understanding. Unlike Inferential statistics, Descriptive statistics seeks to describe the data, however, it does not attempt to draw inferences from the sample to the whole population. We simply describe the data in a sample. It is not developed on the basis of probability unlike Inferential statistics. Descriptive statistics is further broken into two categories – Measure of Central Tendency and Measures of Variability. Inferential statisticsInferential statistics is the method of estimating the population parameter based on the sample information. It applies dimensions from sample groups in an experiment to contrast the conduct group and make overviews on the large population sample. Please note that the inferential statistics are effective and valuable only when examining each member of the group is difficult. Let us understand Descriptive and Inferential statistics with the help of an example. Task – Suppose, you need to calculate the score of the players who scored a century in a cricket tournament.  Solution: Using Descriptive statistics you can get the desired results.   Task – Now, you need the overall score of the players who scored a century in the cricket tournament.  Solution: Applying the knowledge of Inferential statistics will help you in getting your desired results.  Top Five Considerations for Statistical Data AnalysisData can be messy. Even a small blunder may cost you a fortune. Therefore, special care when working with statistical data is of utmost importance. Here are a few key takeaways you must consider to minimize errors and improve accuracy. Define the purpose and determine the location where the publication will take place.  Understand the assets to undertake the investigation. Understand the individual capability of appropriately managing and understanding the analysis.  Determine whether there is a need to repeat the process.  Know the expectation of the individuals evaluating reviewing, committee, and supervision. Statistics and ParametersDetermining the sample size requires understanding statistics and parameters. The two being very closely related are often confused and sometimes hard to distinguish.  StatisticsA statistic is merely a portion of a target sample. It refers to the measure of the values calculated from the population.  A parameter is a fixed and unknown numerical value used for describing the entire population. The most commonly used parameters are: Mean Median Mode Mean :  The mean is the average or the most common value in a data sample or a population. It is also referred to as the expected value. Formula: Sum of the total number of observations/the number of observations. Experimental data set: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20  Calculating mean:   (2 + 4 + 6 + 8 + 10 + 12 + 14 + 16 + 18 + 20)/10  = 110/10   = 11 Median:  In statistics, the median is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. It’s the mid-value obtained by arranging the data in increasing order or descending order. Formula:  Let n be the data set (increasing order) When data set is odd: Median = n+1/2th term Case-I: (n is odd)  Experimental data set = 1, 2, 3, 4, 5  Median (n = 5) = [(5 +1)/2]th term      = 6/2 term       = 3rd term   Therefore, the median is 3 When data set is even: Median = [n/2th + (n/2 + 1)th] /2 Case-II: (n is even)  Experimental data set = 1, 2, 3, 4, 5, 6   Median (n = 6) = [n/2th + (n/2 + 1)th]/2     = ( 6/2th + (6/2 +1)th]/2     = (3rd + 4th)/2      = (3 + 4)/2      = 7/2      = 3.5  Therefore, the median is 3.5 Mode: The mode is the value that appears most often in a set of data or a population. Experimental data set= 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4,4,5, 6  Mode = 3 (Since 3 is the most repeated element in the sequence.) Terms Used to Describe DataWhen working with data, you will need to search, inspect, and characterize them. To understand the data in a tech-savvy and straightforward way, we use a few statistical terms to denote them individually or in groups.  The most frequently used terms used to describe data include data point, quantitative variables, indicator, statistic, time-series data, variable, data aggregation, time series, dataset, and database. Let us define each one of them in brief: Data points: These are the numerical files formed and organized for interpretations. Quantitative variables: These variables present the information in digit form.  Indicator: An indicator explains the action of a community's social-economic surroundings.  Time-series data: The time-series defines the sequential data.  Data aggregation: A group of data points and data set. Database: A group of arranged information for examination and recovery.  Time-series: A set of measures of a variable documented over a specified time. Step-by-Step Statistical Analysis ProcessThe statistical analysis process involves five steps followed one after another. Step 1: Design the study and find the population of the study. Step 2: Collect data as samples. Step 3: Describe the data in the sample. Step 4: Make inferences with the help of samples and calculations Step 5: Take action Data distributionData distribution is an entry that displays entire imaginable readings of data. It shows how frequently a value occurs. Distributed data is always in ascending order, charts, and graphs enabling visibility of measurements and frequencies. The distribution function displaying the density of values of reading is known as the probability density function. Percentiles in data distributionA percentile is the reading in a distribution with a specified percentage of clarifications under it.  Let us understand percentiles with the help of an example.  Suppose you have scored 90th percentile on a math test. A basic interpretation is that merely 4-5% of the scores were higher than your scores. Right? The median is 50th percentile because the assumed 50% of the values are higher than the median. Dispersion Dispersion explains the magnitude of distribution readings anticipated for a specific variable and multiple unique statistics like range, variance, and standard deviation. For instance, high values of a data set are widely scattered while small values of data are firmly clustered. Histogram The histogram is a pictorial display that arranges a group of data facts into user detailed ranges. A histogram summarizes a data series into a simple interpreted graphic by obtaining many data facts and combining them into reasonable ranges. It contains a variety of results into columns on the x-axis. The y axis displays percentages of data for each column and is applied to picture data distributions. Bell Curve distribution Bell curve distribution is a pictorial representation of a probability distribution whose fundamental standard deviation obtained from the mean makes the bell, shaped curving. The peak point on the curve symbolizes the maximum likely occasion in a pattern of data. The other possible outcomes are symmetrically dispersed around the mean, making a descending sloping curve on both sides of the peak. The curve breadth is therefore known as the standard deviation. Hypothesis testingHypothesis testing is a process where experts experiment with a theory of a population parameter. It aims to evaluate the credibility of a hypothesis using sample data. The five steps involved in hypothesis testing are:  Identify the no outcome hypothesis.  (A worthless or a no-output hypothesis has no outcome, connection, or dissimilarities amongst many factors.) Identify the alternative hypothesis.  Establish the importance level of the hypothesis.  Estimate the experiment statistic and equivalent P-value. P-value explains the possibility of getting a sample statistic.  Sketch a conclusion to interpret into a report about the alternate hypothesis. Types of variablesA variable is any digit, amount, or feature that is countable or measurable. Simply put, it is a variable characteristic that varies. The six types of variables include the following: Dependent variableA dependent variable has values that vary according to the value of another variable known as the independent variable.  Independent variableAn independent variable on the other side is controllable by experts. Its reports are recorded and equated.  Intervening variableAn intervening variable explicates fundamental relations between variables. Moderator variableA moderator variable upsets the power of the connection between dependent and independent variables.  Control variableA control variable is anything restricted to a research study. The values are constant throughout the experiment. Extraneous variableExtraneous variable refers to the entire variables that are dependent but can upset experimental outcomes. Chi-square testChi-square test records the contrast of a model to actual experimental data. Data is unsystematic, underdone, equally limited, obtained from independent variables, and a sufficient sample. It relates the size of any inconsistencies among the expected outcomes and the actual outcomes, provided with the sample size and the number of variables in the connection. Types of FrequenciesFrequency refers to the number of repetitions of reading in an experiment in a given time. Three types of frequency distribution include the following: Grouped, ungrouped Cumulative, relative Relative cumulative frequency distribution. Features of FrequenciesThe calculation of central tendency and position (median, mean, and mode). The measure of dispersion (range, variance, and standard deviation). Degree of symmetry (skewness). Peakedness (kurtosis). Correlation MatrixThe correlation matrix is a table that shows the correlation coefficients of unique variables. It is a powerful tool that summarises datasets points and picture sequences in the provided data. A correlation matrix includes rows and columns that display variables. Additionally, the correlation matrix exploits in aggregation with other varieties of statistical analysis. Inferential StatisticsInferential statistics use random data samples for demonstration and to create inferences. They are measured when analysis of each individual of a whole group is not likely to happen. Applications of Inferential StatisticsInferential statistics in educational research is not likely to sample the entire population that has summaries. For instance, the aim of an investigation study may be to obtain whether a new method of learning mathematics develops mathematical accomplishment for all students in a class. Marketing organizations: Marketing organizations use inferential statistics to dispute a survey and request inquiries. It is because carrying out surveys for all the individuals about merchandise is not likely. Finance departments: Financial departments apply inferential statistics for expected financial plan and resources expenses, especially when there are several indefinite aspects. However, economists cannot estimate all that use possibility. Economic planning: In economic planning, there are potent methods like index figures, time series investigation, and estimation. Inferential statistics measures national income and its components. It gathers info about revenue, investment, saving, and spending to establish links among them. Key TakeawaysStatistical analysis is the gathering and explanation of data to expose sequences and tendencies.   Two divisions of statistical analysis are statistical and non-statistical analyses.  Descriptive and Inferential statistics are the two main categories of statistical analysis. Descriptive statistics describe data, whereas Inferential statistics equate dissimilarities between the sample groups.  Statistics aims to teach individuals how to use restricted samples to generate intellectual and precise results for a large group.   Mean, median, and mode are the statistical analysis parameters used to measure central tendency.   Conclusion Statistical analysis is the procedure of gathering and examining data to recognize sequences and trends. It uses random samples of data obtained from a population to demonstrate and create inferences on a group. Inferential statistics applies economic planning with potent methods like index figures, time series investigation, and estimation.  Statistical analysis finds its applications in all the major sectors – marketing, finance, economic, operations, and data mining. Statistical analysis aids marketing organizations in disputing a survey and requesting inquiries concerning their merchandise. 
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What Is Statistical Analysis and Its Business Appl...

Statistics is a science concerned with collection,... Read More