Python 精度分数（numpy.float64'；对象不可调用）

我不知道如何解决这个问题，有人能解释一下吗？

我试图通过改变DecisionTreeClassifier的参数来获得循环中的最佳精度分数

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**

---------------------------------------------------------------------------
TypeError回溯（最近一次调用上次）
在里面
17测试分数=clf分数（X测试、y测试）
18预测=clf.预测（X_检验）
--->19精密度得分=精密度得分（y检验，preds，平均值='micro'）
20
21临时分数=pd.DataFrame（{'depth'：[depth]，
**TypeError:“numpy.float64”对象不可调用**

这是数据集

---

您在周期中的最后几行：

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\u score

定义为numpy数组，然后将其作为函数调用（下一个周期）。

可能是您可以检查

y\u test

和

preds

的结果？