The correct answer is D. All of the mentioned.
In-sample error is the error rate you get on the training dataset. It is also called resubstitution error because you are substituting the training data for the test data. Out-of-sample error is the error rate you get on the test dataset. It is also called generalization error because it measures how well the model generalizes to new data.
In-sample error is always lower than out-of-sample error because the model is trained on the training data. This means that the model is optimized to fit the training data and will therefore do well on the training data. However, this does not mean that the model will do well on new data. Out-of-sample error measures how well the model generalizes to new data. This is a more important measure of the model’s performance because it shows how well the model will perform on real-world data.
It is important to note that in-sample error and out-of-sample error can be very different. This is because the model is optimized to fit the training data, which means that it will do well on the training data but may not do well on new data. This is why it is important to evaluate a model’s performance on out-of-sample data.