# Regression

### What is Regression?

Predicting a continuous numerical value (ex: wage, selling price, etc.) is Regression.

### Differentiate between Linear Models and Non Linear Models

Linear models are a class of models in which a response variable is linearly related to one or more predictors.

### What is Linear Regression?

Linear regression is a statistical technique that relates the mean, or expected value, of a continuous response variable through a weighted combination of one or more independent predictor variables.

### What are the assumptions of linear regression?

Some of the assumptions of the linear regression model includes independence, normality, constant variance and linearity

### How are coefficients of linear regression estimated?

Linear regression minimizes the squared difference between the actual values of the response and the predicted values from the model.

### What are some of the evaluation criteria used to assess the fit of a Linear Regression model?

Global F-test, R-Squared, MSE, MAE, RMSE, Information Criteria (AIC, BIC)

### What is Regularization?

Regularization involves adding a penalty for complexity to the model objective function to improve a model’s generalization performance.

### What does L1 regularization (Lasso) mean?

L1 regularization, or LASSO (Least Absolute Shrinkage and Selection Operator), is a kind of regularization

### What does L2 regularization (Ridge) mean?

L2, or Ridge regularization, is a form of regularization in which the penalty is based on the squared magnitude of the coefficients.