Which regularization technique is most suitable when you suspect that only a subset of features has a significant impact on the target variable?Review LaterL1 Regularization (Lasso)L2 Regularization (Ridge)ElasticNet RegularizationNone of the above

Which technique serves as a form of regularization by restricting the exploration of parameter space from the initial parameters, and requires validation data to determine its timing?*a) Lasso regressionb) Dropoutc) Early stoppingd) Ridge regression

Question 5Which one of the 3 Regularization techniques: Ridge, Lasso, and Elastic Net, performs the fastest under the hood? 1 pointRidgeLassoElastic NetNone of the above

Which of the following is NOT a type of regularization technique used in linear regression to prevent overfitting?

Which of the following statements about regularization techniques is false?Question 9AnswerA.Regularization reduces the effective number of features used by the modelB.Regularization helps to combat overfitting.C.Regularization shrinks the weights of less important features towards zero.D.Regularization increases the model bias

ll of the following statements about Regularization are TRUE except:1 pointOptimizing predictive models is about finding the right bias/variance tradeoff.Features should rarely or never be scaled prior to implementing regularization.We need models that are sufficiently complex to capture patterns in data, but not so complex that they overfit.Regularization techniques have an analytical, a geometric, and a probabilistic interpretation.