When splitting your data, what is the purpose of the training data?1 pointCompare with the actual valueFit the actual model and learn the parametersPredict the label with the modelMeasure errors

1.Question 1The main purpose of splitting your data into a training and test sets is: 1 pointTo improve accuracyTo avoid overfittingTo improve regularizationTo improve crossvalidation and overfitting

3. Why do you split data into training and validation sets? Data is split into two sets in order to create two models, one model with the training set and a different model with the validation set.Splitting data into two sets enables you to train the model with the training set and test the model on unseen data from the test set.Only split data when you use the Azure Machine Learning Designer, not in other machine learning scenarios.

Question 2Complete the following sentence: The training data is used to fit the model, while the test data is used to:1 pointmeasure the parameters and hyperparameters of the modeltweak the model hyperparameterstweak the model parametersmeasure error and performance of the model

Data used to optimize the parameter settings of a supervised learner model is called ...............(1 Point)TestTrainingValidationNone of the above

Which function in scikit-learn is used to split data into training and testing sets?Answer areatrain_test_split()split_data()data_split()train_test()