我会使用sklearns的cross_瓦尔_score来实现这个目标：

from sklearn.model_selection import cross_val_score
X = test_df[['histor', 'philosoph', 'cook', 'roman', 'bibl']]
y = test_df["label"].values
cross_val_score(baseline, X, y, cv=10)

您可以将准确度得分存储在列表中，然后使用该列表计算最后的平均准确度

import numpy as np
 n = 10
accuracies = np.zeros(n)
for i in range(n):
    predictions = test_df[['histor', 'philosoph', 'cook', 'roman', 'bibl']].apply(lambda x: baseline.predict(*x), axis=1)
    accuracy = accuracy_score(y_true, predictions)
    accuracies[i] = accuracy
    print("Run ", i+1, " Accuracy: ", accuracy)

mean_accuracy = np.mean(accuracies)
print("Mean Accuracy: ", mean_accuracy)

或

n = 10
accuracies = []
for i in range(n):
    predictions = test_df[['histor', 'philosoph', 'cook', 'roman', 'bibl']].apply(lambda x: baseline.predict(*x), axis=1)
    accuracy = accuracy_score(y_true, predictions)
    accuracies.append(accuracy)
    print("Run ", i+1, " Accuracy: ", accuracy)

mean_accuracy = sum(accuracies) / n
print("Mean Accuracy: ", mean_accuracy)