What is the main objective to split the time series data in to training and test set?Answer choicesSelect only one optionREVISITBoth,training and test set are used to train the forecasting modelThe training set is used to train the forecasting model and the Test set is used to validate the modelThe training set is used to train and evaluate the model and the test set is used to check the model performanceThe training set is used to train the forecasting model and the test set is to retrain the forecasting model

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

Regarding splitting datasets into training, validation, and test partitions, which ofthe following statements is true, if any?(i) The validation set is used multiple times to choose the best value forhyperparameters.(ii) The test set is used only once to determine the performance on unseen data.(iii) Improving performance on the validation set always improves performance onthe test set.

Which technique is used for evaluating time series models by simulating forecasting performance on multiple training and test sets?Review LaterCross-validationBootstrappingTime series decompositionAutocorrelation analysis

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()

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.