1. How many of the following statements are True?

As model complexity increases, the mean squared error on the test data will tend to decrease.

As model complexity increases, the mean squared error on the training data will tend to decrease.

As model complexity increases, the model bias will tend to increase.

As model complexity increases, the model variance will tend to increase.

2. How many of the following statements are True?

Best subset selection suffers from combinatorial explosion as it requires the estimation of 2P different models, where p is the number of available features.

Forward selection and backward selection belong to the family of stepwise selection methods.

Complete subset regression is used to identify the best subset of k features, where k is less than the total number of available predictors.

Best subset selection is a special case of complete subset regression if the residual sum of squares (RSS) is used as a metric to compare models.

Please answer correctly and fastly otherwise please leave it to others