What statement is True? 1 pointWe reduce the complexity of the model by minimizing the error on our training set. By penalizing the cost function, we increase the complexity of the model. The goal of Regularization is always going to be to optimize our complexity trade off, so we can minimize error on the hold-out set. Introducing Regularization will increase bias and variance.

Question 3Which of the following statements about regularization is TRUE? 1 pointRegularization always reduces the number of selected features. Regularization increases the likelihood of overfitting relative to training data. Regularization decreases the likelihood of overfitting relative to training data.Regularization performs feature selection without a negative impact in the likelihood of overfitting relative to the training data.

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.

Q.No 9. Regularization techniques in linear regression aim to:a) Improve the interpretability of the modelb) Reduce the variance of the model and prevent overfittingc) Increase the complexity of the modeld) Decrease the bias of the model

What is the primary purpose of regularization techniques in deep learning?Question 2AnswerA.To increase model complexityB.To introduce noise in the dataC.To reduce model biasD.To increase model variance

why regularisation improve overfitting