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.