Regression Inside story

#linear #regression #polynomial #ridge #lasso

Akash Deep Nov 23 2021 · 1 min read
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What is regression? Which models can you use to solve a regression problem ?

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).
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