标签：attr features ini main trace recent 注释 imp val
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:595: 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:595: 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:595: 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:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler. warnings.warn(msg, DataConversionWarning) 8999 3000 3000 0 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 130, 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 124, 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 116, in hr_modeling filled=True, rounded=True, special_characters=True) File "C:\Python36\lib\site-packages\sklearn\tree\export.py", line 396, in export_graphviz check_is_fitted(decision_tree, ‘tree_‘) File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line 951, 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 1
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([[1, 2], [2, 3], [3, 4]]) ... except NotFittedError as e: ... print(repr(e)) ... NotFittedError(‘This LinearSVC instance is not fitted yet‘...)
先调用fit方法再进行预测
clf = clf.fit(X_train, Y_train)
Y_pred = clf.predict(DecisionTreeClassifier())
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=3))) 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
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
标签：attr features ini main trace recent 注释 imp val
原文地址：https://www.cnblogs.com/Java-Starter/p/10141649.html
