Regression in Machine Learning – Definition, Types, & More

A nineteenth-century scientist, Sir Francis Galton, conceptualized the term ‘regression’. What was introduced to study the relationship between the heights of fathers and sons is now a common statistical technique in machine learning for investigating and modeling relationships between variables. This blog discusses what is regression in machine learning, its various types, important terminologies related to regression, and more.

Introduction

Machine learning models have various applications, but the most common application of machine learning is solving regression problems. These algorithms are trained to investigate the relationship between the outcome or dependent variable and features or independent variable. They are useful as they help predict an outcome of unseen and new data. It is also useful in filling a missing gap in data.

For any predictive model or forecasting analysis, regression in machine learning is one of the key parts, without which it cannot function. It is also commonly used for supervised machine learning along with classifications. This method of training requires labeled input and output-training data. The regression models in machine learning require understanding the relationship between the features and outcome variables, which is why it is essential to label training data.

Regression analysis helps to bring key insights, whether trying to predict healthcare trends or forecast financial markets.

What is Regression in ML?

In machine learning, regression is a method used to understand the relationship between the outcome or dependent variable and features or independent variable. Once the relationship is established between dependent and independent variables, outcomes can be predicted. 

It is one of the key parts of machine learning and is used to predict continuous outcomes. Its utilization is in predicting and forecasting outcomes from the given data. This commonly involves plotting a line through data points and minimizing the distance between them to achieve the best-fit line.

Regression models can be trained to predict or forecast trends in various industries based on input data, these include:

• The healthcare industry, 
• The automotive industry. ,
• The finance investment industry, or more.

To know more about regression and how it works in various industries, you can opt for an in-depth machine learning course.

Important Terminologies Related to Machine Learning

Some of the important terminologies related to regression are as follows:

• Dependent Variable: It is the main factor in regression analysis that one wants to predict or understand. It is also known as the target variable. 
• Independent Variable: It is used to predict the values of the dependent variable. It is also known as a predictor.
• Outliers: It is an observation in a dataset that has either a very high or low value as compared to other observations. Since it is an extreme value, it may hamper the results. Hence, it should be avoided. 
• Multicollinearity: It is a condition where independent variables highly correlate to each other. Several regression techniques assume that it should not be present in a dataset because it causes problems in ranking variables. 
• Underfitting and Overfitting: Overfitting is when the algorithm works well with the training dataset but not with the test dataset. It is also called the problem of high variance. Underfitting is when the algorithm does not work well with the training dataset. It is also called the problem of high bias. 
• Autocorrelation: It tells the errors associated with one observation do not relate to the errors of other observations. 
• Heteroscedasticity: It is a condition where a dependent variable’s variability is not equal across values of an independent variable.

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Types of Regression in Machine Learning

Let us now take a look at the most common regression methods in machine learning.

• Simple Linear Regression
• Multiple Linear Regression
• Logistic Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Ridge Regression
• Lasso Regression
• Principal Components Regression 
• Poisson Regression

Simple Linear Regression

It is a linear regression technique in which a straight line is plotted among data points to minimize the error between the line and various data points. It is the most common and basic machine learning regression type. The relationship between the dependent variable and independent variable is seen to be linear in this method.

Multiple Linear Regression

It is a linear regression technique using more than one independent variable. One example of multiple linear regression is the polynomial regression model. This model is used when there is more than one independent variable. It achieves a much better fit when compared to simple linear regression, especially when multiple independent variables are used. When plotted, the outcome would be a curved line fitted to data points.

Logistic Regression

This regression technique is used when the dependent variable has two values, such as success or failure or true or false. This regression in the machine learning model is used to help predict the dependent variable occurring probability. Commonly the output values are binary. A sigmoid curve is used to map the relationship between the independent variable and the dependent variable.

Polynomial Regression

It is a type of regression that models the nonlinear dataset using a linear model. The original features in this technique are transformed into polynomial features of a given degree and then modeled with the help of a linear model. In a polynomial regression, the relationship between independent (X) and dependent variables (Y) is denoted by the n-th degree.

Support Vector Regression

It is a regression algorithm that works with continuous variables and can be used to solve both linear and nonlinear problems. It is productive as a real value function estimation. In the support vector regression, a hyperplane with a maximum margin is determined to cover the maximum number of data points within that margin. A hyperplane is a line that helps predict continuous variables and covers most data points.

Decision Tree Regression

It is a tree-like structure. The test for an attribute is represented by each internal node, the result of the test by each branch, and the final result by each leaf node. The decision tree is created from a root node, the dataset. It then splits into left and right child nodes or subsets of a dataset. These child nodes further divide into children nodes.

Random Forest Regression

It is an ensemble learning method that combines multiple decision trees and predicts the outcome as the average outcome of each tree. It uses bagging or bootstrap techniques where the aggregate decision trees run parallel to each other. Using random forest regression, one can create random subsets of a dataset to prevent overfitting in a model.

Ridge Regression

It is a type of regression used in case of a high correlation between independent variables. In ridge regression, a small amount of bias is introduced to get better long-term predictions. The amount of bias introduced is called the Ridge Regression Penalty. This technique is also called L2 regularization due to it being a regularization technique that reduces the complexity of the model. It helps control overfitting.

Lasso Regression

It is used to perform regularization along with feature selection. Though it is similar to ridge regression, the penalty term here contains only the absolute weights instead of a square of weights. It can shrink the coefficient to exact zero and is also called L1 regularization.

Principal Components Regression

It is used when working with various independent variables. This technique helps assume the unknown regression coefficient in a standard linear regression model. First, we obtain the principal components and then the regression analysis is done.

Poisson Regression

It is used when the dependent variable has a calculation. The dependent variable (y) has a Poisson distribution. When used to model contingency tablets, it is called a log-linear model. Usually, it is used to show the number of calls related to a product in customer care.

Why Use Regression Analysis?

Regression analysis is used for the following reasons.

• It is useful in the prediction of a continuous variable.
• It estimates the association between the target and the independent variable.
• It can show the magnitude of the association between variables and infer its statistical significance. 
• It helps predict future outcomes based on past observations. 
• It helps one recognize the most important factor, the least important factor, and how each factor affects other factors. 
• It is also useful in finding trends in data.

Conclusion

With machine learning, important business decisions can be made, making work more efficient and saving both time and money. Regression in machine learning further facilitates this as it is used as predictive modeling to predict continuous outcomes. It is lucrative since various types of regression techniques help in processing different kinds of data.