Types of Regression Techniques Guide
Types of Regression Techniques, Regression analysis is used to examine how changes in an independent variable affect the dependent variable.
Basically, Regression analysis involves creating an equation to describe the significant association between one or more predictors and response variables, as well as estimating current observations.
The results of the regression reveal the direction, size, and analytical significance of the relationship between predictor and response, where the dependent variable is either numerical or discrete.
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When Regression is chosen?
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 can be utilized, the most basic of which is linear regression.
It tries to fit the data to the best hyperplane that passes through all of the locations.
Regression Analysis is a statistical method for assessing the associations between one or more independent variables or predictors and the dependent variables or criterion variables.
The changes in criterion in connection to changes in select predictors are explained using regression analysis.
Determining the strength of predictors, anticipating an impact, and trend forecasting are three primary applications of regression analysis.
Types of Regression techniques
1. Linear Regression
For predictive analysis, linear regression is performed. Linear regression is a method for modeling the relationship between criteria or scalar response and many predictors or explanatory factors using a linear approach.
The conditional probability distribution of the response given the values of the predictors is the focus of linear regression.
There is a risk of overfitting in linear regression.
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2. Polynomial Regression
For curved data, polynomial regression is utilized. The least-squares method 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.
3. Stepwise Regression
For fitting regression models with predictive models, stepwise regression is utilized. It is carried out in an automated manner.
The variable is added or deleted from the set of explanatory variables at each stage.
Forward selection, backward elimination, and bidirectional elimination are three methods for stepwise regression.
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4. Ridge Regression
Ridge regression is a method of assessing data from several regression models. The least-squares estimates are unbiased when multicollinearity occurs.
Ridge regression reduces the standard errors by adding a degree of bias to the regression estimates.
5. Lasso Regression
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 provided covariates is used in the final model with Lasso regression.
6. ElasticNet Regression
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 use ElasticNet regression.