Hence, the model will be less likely to fit the noise of the training data and will improve the generalization abilities of the model.Let me put it this way imagine parents sometimes take a decision about how much flexibility should be given to children during their upbringing..Too much restriction may suppress their development of character..Alternatively, too much flexibility may spoil their future..The answer to this question is regularized flexibility, which is to give enough flexibility added with regularization.There are two types of regularization as follows:Lasso Regularization or L1 Regularization.Ridge Regularization or L2 Regularization.LASSO (Least Absolute Shrinkage and Selection Operator) a powerful feature selection technique that is very useful for regression problems..Lasso is essentially a regularization method..It adds a penalty for non-zero coefficients, lasso penalizes the sum of their absolute values (L1 penalty)..As a result, for high values of λ, many coefficients are exactly zeroed under lasso, which is never the case in ridge regression.L1 Regularization.L2 regularization is also known as ridge regression or Tikhonov regularization..The L2 parameter norm penalty is commonly known as weight decay..This regularization strategy drives the weights closer to the origin 1 by adding a regularization term to the objective function.Ridge Regularization.Thanks for reading..If you enjoyed this article, feel free to hit that follow button to stay in touch.. More details