Q20. What is regression? Which models can you use to solve a regression problem?

A20: Regression is a part of supervised ML. Regression models investigate the relationship between a dependent (target) and independent variable (s) (predictor). Here are some common regression models

• Linear Regression establishes a linear relationship between target and predictor (s). It predicts a numeric value and has a shape of a straight line.
• Polynomial Regression has a regression equation with the power of independent variable more than 1. It is a curve that fits into the data points.
• Ridge Regression helps when predictors are highly correlated (multicollinearity problem). It penalizes the squares of regression coefficients but doesn’t allow the coefficients to reach zeros (uses L2 regularization).
• Lasso Regression penalizes the absolute values of regression coefficients and allows some of the coefficients to reach absolute zero (thereby allowing feature selection).

Q21. What is linear regression? When do we use it?

A21: Linear regression is a model that assumes a linear relationship between the input variables (X) and the single output variable (y).

With a simple equation:

y = B0 + B1*x1 + ... + Bn * xN

“B” is regression coefficients, “x” values are the independent (explanatory) variables and “y” is dependent variable.

The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.

Simple linear regression:

y = B0 + B1*x1

Multiple linear regression:

y = B0 + B1*x1 + ... + Bn * xN

#data-science-courses #interview-preparation #machine-learning #data-science #interview-questions