Lasso Regression

  Python implementation of the Lasso Regression algorithm Prerequisites: Linear Regression  Gradient Descent Introduction: Another linear model that is descended from Linear Regression and uses the same fictitious function for prediction is Lasso Regression. J is a representation of the linear regression's cost function. The dataset's total number of training examples, m, is indicated here. The hypothetical prediction function represents h(x(i)). The target variable's value for the ith training example is represented by y(i). The Linear Regression model considers all the features equally relevant for prediction. when a dataset contains many features, even if some of them are irrelevant to the predictive model. Due to the test set's too incorrect forecast, the model becomes increasingly sophisticated ( or overfitting ). A high variance model like this does not generalize to the new data. Lasso Regression steps in to save the day. It changed the cost function of linear re...