NO.PZ2015120204000035
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:
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.
不知道这道题在考什么点,题目有什么关键词是提到了这个点吗