前半句话没听懂

问题如下：

Steele and Schultz discuss the importance of feature selection and feature engineering in ML model training. Steele tells Schultz:

“Appropriate feature selection is a key factor in minimizing model overfitting, whereas feature engineering tends to prevent model underfitting.”

Is Steele’s statement regarding the relationship between feature selection/feature engineering and model fit correct?

选项：

A.

Yes

B.

No, because she is incorrect with respect to feature selection.

C.

No, because she is incorrect with respect to feature engineering.

解释：

A is correct. A dataset with a small number of features may not carry all the characteristics that explain relationships between the target variable and the features. Conversely, a large number of features can complicate the model and
potentially distort patterns in the data due to low degrees of freedom, causing overfitting. Therefore, appropriate feature selection is a key factor in minimizing such model overfitting. Feature engineering tends to prevent underfitting in the training of the model. New features, when engineered properly, can elevate the underlying data points that better explain the interactions of features. Thus, feature engineering can be critical to overcome underfitting.