HomeBlogData ScienceLinear Regression in Machine Learning: A Comprehensive Guide
Statistical techniques have been used for Data Analysis and Interpretation for a long time. Linear Regression in Machine Learning analysis is important for evaluating data and establishing a definite relationship between two or more variables. Regression quantifies how the dependent variable changes as the independent variable itself take different values. Regression is referred to as simple or multiple regression depending on the number of independent variables, like single or multiple variables respectively.
Machine Learning is the solution when data is large, and relation becomes difficult to quantify manually. Here, the model is trained on available data of a number of independent variables with the statistical tool of Linear Regression to determine how the relationship can be obtained with great accuracy. This article has a practical example of Regression in Machine Learning for beginners. These days a comprehensive Data Science online course can help build the necessary foundation to the essential concepts of Regression in Machine Learning.
Linear Regression is an algorithm that belongs to supervised Machine Learning. It tries to apply relations that will predict the outcome of an event based on the independent variable data points. The relation is usually a straight line that best fits the different data points as close as possible. The output is of a continuous form, i.e., numerical value. For example, the output could be revenue or sales in currency, the number of products sold, etc. In the above machine learning example in linear regression, the independent variable can be single or multiple.
Linear regression can be expressed mathematically as:
y= β0+ β 1x+ ε
Here,
The last parameter, random error ε, is required as the best fit line also doesn't include the data points perfectly.
Since the Linear Regression algorithm represents a linear relationship between a dependent (y) and one or more independent (y) variables, it is known as Linear Regression. This means it finds how the value of the dependent variable changes according to the change in the value of the independent variable. The relation between independent and dependent variables is a straight line with a slope.
My path with linear regression in machine learning has made me realize how the best line of fit plays a pivotal role in this process. This one reckons the meaning of my data under the independent variable(s) and dependent variable relationship. The definition can be slightly paraphrased as the variable sets the dependent one in motion, as the independent variable(s) that causes its change.
The best fit line is selected among those minimizing the sum of the squares of those (observed) values that differ from the (predicted) values, which is known as the least squares formulation. This will lead to the line being as aligned to the data points as possible by minimizing any type of error occurring. The formula of this line is usually
y = mx + b
The output is a continuous value, integer, or probability percentage based on the selected problems. Thus, it can be sales amount, profit percentage, probability of success or failure in some activities like admission possibility, winning an election, etc. With the best fit line of regression, the output value for any new value of the input variable can be easily calculated.
There are many benefits of Linear Regression, including simplicity of understanding and implementation. It can be applied to obtain relations in linear or multi-linear parameters and thus can be applied to various business problems.
A Linear Regression model will use a mathematical equation to derive a relation between a predicted variable which varies with independent variables. The best fit line will be obtained based on the given data after applying the algorithm, and this line can then be used to give expected predictions.
Many datasets can be used for Linear Regression, like stock price prediction, house price prediction, disease prediction probability, medical insurance costs, etc.
Although it is not easy to specify a particular best ML model for regression yet, one can select a regression model that best fits to predict outcomes of numerical nature. A multi-Linear Regression model would probably be a good choice in most cases.
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