Regression analysis is often used in banking, investment, and other fields to determine the connection between a single dependent variable (goal variable) and several independent variables. For instance, anticipating the price of a home, the stock market, or an employee’s compensation are among the most prevalent forms of prediction.
Here are the Machine Learning (ML) regression algorithms:
Linear Regression is an ML technique utilized for supervised learning. The objective is to forecast a dependent variable (goal) based on the provided independent variable(/s). Tis algorithm regression method establishes a linear connection between predictor variables and the other independent variables.
The Decision Tree structures may be used for any data sets with numerical and categorical characteristics. Decision Trees are effective at representing nonlinear interactions between the attributes and the goal variable. Understanding the data is fairly intuitive due to the fact that decision trees resemble human thought processes.
Support Vector Regression (SVR) employs the same concept as Support Vector Machine (SVM), but attempts to predict actual values. This approach separates the data using hyperplanes. In the event that this separation is not achievable, a kernel technique is used wherein the dimension is raised and the data points become hyperplane-separable.
In Least Absolute Selection Shrinkage Operator (LASSO), shrinkage is a limitation on traits or parameters. This technique identifies and imposes a restriction to model features that causes regression coefficients for certain variables to approach zero.
The model excludes variables having a regression coefficient of zero.
LASSO Regression analysis is essentially a contraction and variable selection technique that helps discover which predictors are the most significant.
Random Forest Regressor
Random Forests are groups of Decision Trees. It is an approach for classification and regression that is based on Supervised Learning. The data supplied is processed by numerous decision trees. It operates by creating a variable number of decision trees during training and outputs the class that is the mode of the classes or the mean prediction (for regression) of the individual trees.