用 scikit-learn 和 Pandas 制作机器学习模型，但我认为它的准确性有问题

Question

我正在使用 Pandas 和 scikit-learning 处理分类模型。 我发现模型的准确率太高而且都一样，我不知道为什么

decision_tree = tree.DecisionTreeClassifier()
decision_tree = decision_tree.fit(X_train,y_train)
print("Test set accuracy = ", model.score(X_test, y_test))

Test set accuracy =  0.9615384615384616

logistic_regression = LogisticRegression()
logistic_regression.fit(X_train,y_train)
print("Test set accuracy = ", model.score(X_test, y_test))

Test set accuracy =  0.9615384615384616

support_vector = SVC()
support_vector.fit(X_train,y_train)
print("Test set accuracy = ", model.score(X_test, y_test))

Test set accuracy =  0.9615384615384616

我预计分类模型的准确率会有所不同，但事实并非如此，我认为它的准确率太高了。 但我不知道出了什么问题:(如果你能帮忙，我将非常感谢..

Answer 1

从评论中，您需要运行此代码：

decision_tree = tree.DecisionTreeClassifier()
decision_tree = decision_tree.fit(X_train,y_train)
print("Test set accuracy = ", decision_tree.score(X_test, y_test))


logistic_regression = LogisticRegression()
logistic_regression.fit(X_train,y_train)
print("Test set accuracy = ", logistic_regression.score(X_test, y_test))



support_vector = SVC()
support_vector.fit(X_train,y_train)
print("Test set accuracy = ", support_vector.score(X_test, y_test))

在您的原始代码中，您始终运行代码中未定义的model ，这就是您获得相同分数的原因