问题如下：
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：
A.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.
请问 K-fold cross validation 在讲义里哪里有提到？谢谢
