精度分数（numpy.float64'对象不可调用）

Question

我不知道如何解决这个问题，有人能给我解释一下吗？我正在努力通过更改 DecisionTreeClassifier 的参数来在循环中获得最佳 precision_scoreimport pandas as pdfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.metrics import precision_scorefrom sklearn.model_selection import train_test_split        df = pd.read_csv('songs.csv')        X = df.drop(['song','artist','genre','lyrics'],axis=1)    y = df.artist        X_train,X_test,y_train,y_test = train_test_split(X,y)        scores_data = pd.DataFrame()    for depth in range(1,100):        clf = DecisionTreeClassifier(max_depth=depth,criterion='entropy').fit(X_train,y_train)        train_score = clf.score(X_train,y_train)        test_score = clf.score(X_test,y_test)        preds = clf.predict(X_test)        precision_score = precision_score(y_test,preds,average='micro')                temp_scores = pd.DataFrame({'depth':[depth],                                    'test_score':[test_score],                                     'train_score':[train_score],                                     'precision_score:':[precision_score]})        scores_data = scores_data.append(temp_scores)        这是我的错误：---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-50-f4a4eaa48ce6> in <module>     17     test_score = clf.score(X_test,y_test)     18     preds = clf.predict(X_test)---> 19     precision_score = precision_score(y_test,preds,average='micro')     20      21     temp_scores = pd.DataFrame({'depth':[depth],**TypeError: 'numpy.float64' object is not callable**这是数据集https://i.stack.imgur.com/QJXSY.png

Answer 1

循环中的最后一行：

precision_score = precision_score(y_test,preds,average='micro')

temp_scores = pd.DataFrame({'depth':[depth],

                            'test_score':[test_score],

                             'train_score':[train_score],

                             'precision_score:':[precision_score]})

scores_data = scores_data.append(temp_scores)

应改为：

precision_score_ = precision_score(y_test,preds,average='micro')

temp_scores = pd.DataFrame({'depth':[depth],

                            'test_score':[test_score],

                             'train_score':[train_score],

                             'precision_score:':[precision_score_]})

scores_data = scores_data.append(temp_scores)

您定义precision_score为 numpy 数组，然后像函数一样调用它（下一个周期）。