How Lasso Regression Works in Machine Learning

Regularization solves the problem of overfitting. It happens when the model learns the data as well as the noises in the training set. Noises are random datum in the training set which don't represent the actual properties of the data. Y represents the dependent variable, X represents the independent variables and C represents the coefficient estimates for different variables in the above linear regression equation. The model fitting involves a loss function known as the sum of squares.