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mini · 2020年08月22日

问一道题:NO.PZ2015120204000050

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 over
fitting. 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.

whereas feature engineering tends to prevent model underfitting.” 后面半句话没懂,难道可以阻止过少匹配?

1 个答案
已采纳答案

星星_品职助教 · 2020年08月22日

同学你好,

underfitting(欠拟合)指的是模型没有抓住数据集中的规律,导致拟合的不好。

feature engineering可以使得features更好的去捕捉到规律,所以可以降低模型欠拟合的问题。