不知道这道题在考什么点

问题如下：

Paul suggests the following step which would be repeated every quarter.

Step 3 For each of the 20 different groups, we use labeled data to train a model that will predict the five stocks (in any given group) that are most likely to become acquisition targets in the next one year.

Assuming a Classification and Regression Tree (CART) model is used to accomplish Step 3, which of the following is most likely to result in model overfitting?

选项：

Using the k-fold cross validation method

Including an overfitting penalty (i.e., regularization term)

Using a fitting curve to select a model with low bias error and high variance error

解释：

C is correct. A fitting curve shows the trade-off between bias error and variance error for various potential models. A model with low bias error and high variance error is, by definition, overfitted.

A is incorrect, because there are two common methods to reduce overfitting, one of which is proper data sampling and cross-validation. K-fold cross validation is such a method for estimating out-of-sample error directly by determining the error in validation samples.

B is incorrect, because there are two common methods to reduce overfitting, one of which is preventing the algorithm from getting too complex during selection and training, which requires estimating an overfitting penalty.