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# Regression

Different types of regression techniques

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There are different types of regression techniques in machine learning:

They are:

1. Linear Regression
2. Logistic Regression
3. Stepwise Regression
4. Ridge Regression
5. Lasso Regression
6. Polynomial Regression
7. ElasticNet Regression
8. Bayesian Linear Regression
9. Support Vector Regression
10. Decision Tree Regression
11. Random Forest Regression
12. Boosting Tree Regression
13. XGBoost Regression
14. LightGBM Regression
15. CatBoost Regression

Linear Regression is the most basic and commonly used regression technique. It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the data.

Logistic Regression is used when the dependent variable is binary (0 or 1, true or false, etc.) and you want to predict the probability that the dependent variable will be a 1.

Stepwise Regression is a type of linear regression that automatically selects the variables to include in the model by sequentially adding and removing variables based on their statistical significance.

Ridge Regression is a type of linear regression that adds a penalty to the coefficients to prevent overfitting.

Lasso Regression is a type of linear regression that adds a penalty to the coefficients to prevent overfitting and also performs variable selection by setting some coefficients to zero.

Polynomial Regression is a type of linear regression that can be used to model non-linear relationships.

ElasticNet Regression is a type of linear regression that combines the penalties of Ridge and Lasso regression.

Bayesian Linear Regression is a type of linear regression that uses Bayesian inference to estimate the coefficients.

Support Vector Regression is a type of regression that uses support vector machines to model the relationship between the dependent and independent variables.

Decision Tree Regression is a type of regression that builds a decision tree to model the relationship between the dependent and independent variables.

Random Forest Regression is a type of regression that builds multiple decision trees and averages the predictions to reduce the variance.

Boosting Tree Regression is a type of regression that sequentially builds decision trees and uses the predictions of the previous trees to improve the accuracy of the next tree.

XGBoost Regression is a type of regression that uses gradient boosting to improve the accuracy of the predictions.LightGBM Regression is a type of regression that uses light gradient boosting to improve the accuracy of the predictions.

CatBoost Regression is a type of regression that uses category-based gradient boosting to improve the accuracy of the predictions.

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