修改代码：from sklearn.feature_selection import SelectKBest from sklearn.model_selection import train_test_split abalone = pd.get_dummies(abalone) select = SelectKBest(k = 4) x_ab = abalone.drop('Rings',axis =1) y_ab = abalone['Rings'] x_ab_train, x_ab_test, y_ab_train, y_ab_test = train_test_split(x_ab,y_ab) select.fit(x_ab_train, y_ab_train) X_train_selected = select.transform(x_ab_train) print('特征选择结果：{}'.format(X_train_selected.shape))

import pandas as pd
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.model_selection import train_test_split

abalone = pd.read_csv('abalone.csv')
abalone = pd.get_dummies(abalone)
select = SelectKBest(score_func=f_regression, k=4)
x_ab = abalone.drop('Rings', axis=1)
y_ab = abalone['Rings']
x_ab_train, x_ab_test, y_ab_train, y_ab_test = train_test_split(x_ab, y_ab)
select.fit(x_ab_train, y_ab_train)
X_train_selected = select.transform(x_ab_train)

print('特征选择结果：{}'.format(X_train_selected.shape))

这里我对代码进行了一些修改：

1. 导入了 f_regression 作为评价指标，这是一种基于线性回归模型的特征选择方法，用于评估每个特征与目标变量之间的线性关系。

2. 将 SelectKBest 的参数 k 设为 4，即选择 4 个最优特征。

3. 使用 pd.read_csv() 导入数据集，因为我不确定你是如何导入 abalone 数据的。