精度分数（numpy.float64' 对象不可调用）答案

【问题标题】：precision score (numpy.float64' object is not callable)精度分数（numpy.float64' 对象不可调用）
【发布时间】：2021-01-27 03:19:48
【问题描述】：

我不知道如何解决这个问题，谁能解释一下？

我想通过更改 DecisionTreeClassifier 的参数来获得循环中的最佳precision_score

import pandas as pd

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import precision_score

from 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

【问题讨论】：

也许您可以在y_test 和preds 上查看您获得的信息？

标签： python pandas scikit-learn decision-tree

【解决方案1】：

你在循环中的最后几行：

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)

应该改为：

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)

您将 precision_score 定义为 numpy 数组，然后像调用函数一样调用它（下一个循环）。

【讨论】：