Types of Regression

What is the difference between Ridge Regression, the LASSO, and ElasticNet? tldr: “Ridge” is a fancy name for L2-regularization, “LASSO” means L1-regularization, “ElasticNet” is a ratio of L1 and L2 regularization.

Yes, elastic net is always preferred over lasso & ridge regression because it solves the limitations of both methods, while also including each as special cases. So if the ridge or lasso solution is, indeed, the best, then any good model selection routine will identify that as part of the modeling process.

Linear regression is a basic and commonly used type of predictive analysis. The overall idea of regression is to examine two things: (1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable?

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis.

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x.

Ridge Regression is a remedial measure taken to alleviate multicollinearity amongst regression predictor variables in a model. Often predictor variables used in a regression are highly correlated.

Ridge regression is a method of decreasing the variance of regression parameters by accepting some bias in them. An analogy to a bathroom scale: With ridge regression, we accept a scale that is, on average, 2 pounds light, but never more than 2.5 pounds light or less than 1.5 pounds light instead of a scale that is, on average, correct, but often off by a lot.

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion.

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion.