Why do we split our data into a training set and test set, and then hold back the test set while training our model? We can then use the test set to compare versions of our model and select the best version as our final model We use the test set to calculate the performance of models with different sets of features, to help us with feature selection We select our model using the training set and then add our test set in, re-train our model, and calculate the model's performance across our full dataset We use the test set to evaluate performance of our final model as an unbiased indicator of its ability to generate quality predictions on new data 1 point