Question

Which of the following statements is correct?

a. In-sample errors are the errors that come from predicting results of observations the model has not yet seen
b. The training set serves as a pseudo-reality where we can test our model's performance
c. In-sample errors are the errors that come from predicting the values used to create the model
d. The test set is the protective bubble where we create the model

Answer 1

In-sample errors are the errors that come from predicting the values used to create the model.

Step-by-step explanation:

In the context of statistical modeling, in-sample errors refer to the errors that occur when we predict the values of the data points that were used to create the model. These errors indicate how well the model fits the training data. On the other hand, out-of-sample errors occur when we predict the values of observations that the model has not yet seen. These errors give us an indication of how well the model will perform on new, unseen data.

In summary, option c is correct. In-sample errors are the errors that come from predicting the values used to create the model.