决策树遇到sklearn.exceptions.NotFittedError: XXX instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.的解决方案

1.异常信息：

C:\Python36\python36.exe "E:/python_project/ImoocDataAnalysisMiningModeling/第6章 挖掘建模/6-4~6-5 分类-朴素贝叶斯~分类-决策树.py"
C:\Python36\lib\site-packages\sklearn\utils\validation.py:: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.
  warnings.warn(msg, DataConversionWarning)
C:\Python36\lib\site-packages\sklearn\utils\validation.py:: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.
  warnings.warn(msg, DataConversionWarning)
C:\Python36\lib\site-packages\sklearn\utils\validation.py:: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.
  warnings.warn(msg, DataConversionWarning)
C:\Python36\lib\site-packages\sklearn\utils\validation.py:: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.
  warnings.warn(msg, DataConversionWarning)

Traceback (most recent call last):
KNN ACC: 0.9337704189354372
KNN REC: 0.8670795616960457
  File "E:/python_project/ImoocDataAnalysisMiningModeling/第6章 挖掘建模/6-4~6-5 分类-朴素贝叶斯~分类-决策树.py", line , in <module>
KNN F1 0.8593012275731823
    main()
  File "E:/python_project/ImoocDataAnalysisMiningModeling/第6章 挖掘建模/6-4~6-5 分类-朴素贝叶斯~分类-决策树.py", line , in main
    hr_modeling(features, labels)
  File "E:/python_project/ImoocDataAnalysisMiningModeling/第6章 挖掘建模/6-4~6-5 分类-朴素贝叶斯~分类-决策树.py", line , in hr_modeling
    filled=True, rounded=True, special_characters=True)
  File "C:\Python36\lib\site-packages\sklearn\tree\export.py", line , in export_graphviz
    check_is_fitted(decision_tree, 'tree_')
  File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line , in check_is_fitted
    raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.exceptions.NotFittedError: This KNeighborsClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

Process finished with exit code 

2.错误成因：

2.1 表象原因

Exception class to raise if estimator is used before fitting.

This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility.

大意是在fitting之前使用了estimator

>>> from sklearn.svm import LinearSVC
>>> from sklearn.exceptions import NotFittedError
>>> try:
...     LinearSVC().predict([[, ], [, ], [, ]])
... except NotFittedError as e:
...     print(repr(e))
...
NotFittedError('This LinearSVC instance is not fitted yet'...)

2.2 解决方案：

先调用fit方法再进行预测

clf = clf.fit(X_train, Y_train)
Y_pred = clf.predict(DecisionTreeClassifier())

2.3 根本原因

我在决策树碰到NotFittedError，是因为用到了list，存在多个数学模型，我的代码如下

models = []
    models.append(("KNN", KNeighborsClassifier(n_neighbors=)))
    models.append(("GaussianNB", GaussianNB()))
    models.append(("BernoulliNB", BernoulliNB()))
    # 使用决策树要注释掉前者，否则报NotFittedError
    models.append(("DecisionTree", DecisionTreeClassifier()))
    models.append(("DecisionTreeEntropy", DecisionTreeClassifier(criterion="entropy")))

为什么会报NotFittedError？点击打开"C:\Python36\lib\site-packages\sklearn\tree\export.py"这个文件，会看到

check_is_fitted(decision_tree, 'tree_')

我们可以知道，不是决策树模型就会返回False，因为第一个模型是KNN(K最近邻分类)，不是决策树，所以返回False，返回True需要DecisionTreeClassifier()

这里可以看到，和NotFittedError并无太大关系

2.4 解决方案：

把models前面的模型注释掉，或者重新写一个models将其他数学模型和决策树模型分开以规避这种错误