Understanding Regression in Machine Learning

Regression is a type of supervised Machine Learning algorithm that is used to recreate the relationship between one or more independent variables and dependent variables.

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Purpose and Analysis

Regression analysis helps us understand how the value of the target variable varies with respect to one independent variable while other independent variables remain constant.

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Major Types of Regression

Linear Regression Multiple Linear Regression Polynomial Regression Ridge Regression (L2 Regularization) Lasso Regression (L1 Regularization) Decision Tree Regression Logistic Regression Poisson Regression

Other Types of Regression

Negative Binomial Regression Cox Regression (Proportional Hazards Model Stepwise Regression Time Series Regression Panel Data Regression (Fixed Effects and Random Effects Models) Bayesian Regression Quantile Regression

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Functionality

Regression analysis efficiently finds the line that best fits all data points. This line maximises the model's accuracy by minimising the distance from each data point.

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Conclusion

Regression is a supervised machine-learning technique that assists in determining the correlation between variables. Using regression, you can predict the continuous output variable using one or more predictor variables.

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