Which of the following are some of the main benefits of machine learning over traditional programming? We need lots of "good" quality data We can solve complex problems we might not normally be able to We don't have to explicitly write a long list of rules We can automatically adapt to new data samples learning allows algorithms to generalize from a set of examples by building a model consisting of learned parameters. This model can then be used to make predictions. instance-based structure-based model-based parameter-based We typically break our datasets into two sets: training set and testing set. What is the purpose of the testing set? estimate the genrealization error of your model by simulating unseen data to perform hyperparameter tuning to act as additional data to further train your model Measuring generalization error multiple times on the test set leads to a model that is biased to works best for . The solution to this problem is to use a new data split referred to as the validation set which is used to evaluate the performance of your model. all unseen data all data drawn from the same distribution the test set the train set