You create a ridge regression model to predict housing prices in the Charlotte area. Your model works amazingly well on your training data so you deployed it to the field. However, your model failed to make close to the right predictions with huge errors. What would be the next steps to do? Select all that apply. O Try less number of features Get more training examples Use fewer training examples Transform the input to high degree polynomial features Lower the regularization parameter alpha Question 4 After developing and training a regularized classifier, you observed huge test errors for cancer detection from the recorded voice. While checking the model, you discovered that the metric scores on training samples were not satisfying, either. What can be recommendable next step? Select all that apply. obtain and use additional features transform input features to nonlinear use a smaller number of features 3 pts use a fewer number of samples increase regularization parameter