Types Of Regression Analysis Techniques Training Ppt

Slide 1

This slide states that regression analysis have many types, and the application of each approach is dependent on the number of components. These variables include the kind of target variable, the form of the regression line, and the number of independent variables.

Slide 2

This slide describes that linear regression is one of the most fundamental forms of regression in Machine Learning. The linear regression model links a predictor variable and a dependent variable linearly.

Slide 3

This slide states that Polynomial Regression is a type of regression analysis approach in Machine Learning. It is similar to Multiple Linear Regression but with a few differences. As an estimator, a linear model is used. The Least Mean Squared Method is also used in Polynomial Regression.

Slide 4

This slide states that this sort of regression in Machine Learning is employed when the independent variables have a strong correlation. This is because, in multicollinear data, the least square estimates produce unbiased results. However, if the collinearity is too high, there may be some bias value.

Slide 5

This slide lists that lasso regression is a sort of regression in Machine Learning that includes regularisation and feature selection. It forbids the regression coefficients’ absolute size; and as a result, the coefficient value approaches 0, which is not the case with Ridge Regression.

Slide 6

This slide showcases that Bayesian Regression is a type of Machine Learning regression that uses the Bayes Theorem to determine the value of regression coefficients. Instead of calculating the least-squares, this regression approach determines the posterior distribution of the features.