What are Different type of Regression Techniques? Lean in easiest and simplest ways.
When should Regression be used?
When the output variable is a real or continuous value, such as “salary” or “weight,” you have a regression problem. There are several other models that may be employed, the most basic of which being linear regression. It tries to fit the data to the optimal hyperplane that passes through all of the locations. Regression Analysis is a statistical method for evaluating the connections between one or more independent variables or predictors and the dependent variables or criterion variables. The changes in criterion in response to changes in chosen predictors are explained using regression analysis.
The criteria’s conditional expectation is based on predictors, which give the average value of the dependent variables when the independent variables are modified. Determining the strength of predictors, predicting an impact, and trend forecasting are three important applications of regression analysis.
Regressions come in a variety of shapes and sizes.
- For predictive analysis, linear regression is utilised. Linear regression is a method for modelling the connection between a criteria or scalar response and numerous predictors or explanatory factors using a linear approach. The conditional probability distribution of the answer given the values of the predictors is the focus of linear regression. There is a risk of overfitting in linear regression. Y’ = bX + A is the formula for linear regression.
- For curved data, polynomial regression is employed. The least squares approach is used to fit polynomial regression.
- Regression analysis is used to predict the value of a dependent variable y in relation to an independent variable x. l = beta 0+beta 0x 1+epsilon is the equation for polynomial regression.
- For fitting regression models with predictive models, stepwise regression is employed. It is done out in an automated manner. The variable is added or removed from the collection of explanatory variables at each stage. Forward selection, backward elimination, and bidirectional elimination are three methods for stepwise regression. Stepwise regression is calculated using the formula b j.std = b j(s x * s y-1).
- Ridge regression is a method of evaluating data from several regression models. Least squares estimates are unbiased when multicollinearity exists. Ridge regression decreases the standard errors by adding a degree of bias to the regression estimates. Ridge regression is calculated using the formula beta = (XTX + lambda * I)-1XTy.
- Lasso regression is a regression analysis technique that includes variable selection as well as regularisation. Soft thresholding is used in Lasso regression. Only a subset of the supplied variables are used in the final model with Lasso regression. N-1sumN i=1f(x i, y I, alpha, beta) is the Lasso regression formula.
- ElasticNet regression is a regularised regression approach that combines the lasso and ridge penalties in a linear fashion. Support vector machines, metric learning, and portfolio optimization all employ ElasticNet regression. ||beta|| 1 = sump j=1|beta j| The penalty function is:||beta|| 1 = sump j=1|beta j|