AttributeError：“ DecisionTreeClassifier”对象没有属性“ precision_score”

问题描述

我最近才开始学习数据科学。这是我写的：

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import KFold
 from sklearn.metrics import precision_score,recall_score
 import numpy as np

 #reading data
 df = pd.read_csv('titanic.csv')
 df['male'] = df['Sex'] == 'male'
 X = df[['Pclass','male','Age','Siblings/Spouses','Parents/Children','fare']].values
 y = df['Survived'].values

 #spliting data into train/test
 kf = KFold(n_splits=4+1,shuffle=True,random_state=10)
 tree_scores = {'accuracy_scores':[],'precision_scores':[],'recall_scores':[]}
 logistic_scores = {'accuracy_scores':[],'recall_scores':[]}

 #making the models
 for train_indexes,test_indexes in kf.split(X):
     X_train,X_test = X[train_indexes],X[test_indexes]
     y_train,y_test = y[train_indexes],y[test_indexes]

     tree = DecisionTreeClassifier()
     tree.fit(X_train,y_train)
     tree_scores['accuracy_scores'].append(tree.score(X_test,y_test))
     tree_prediction = tree.predict(X_test)
     #tree_scores['precision_scores'].append(tree.precision_score(y_test,tree_prediction))
     #tree_scores['recall_scores'].append(tree.recall_score(y_test,tree_prediction))

     logistic = LogisticRegression()
     logistic.fit(X_train,y_train)
     logistic_scores['accuracy_scores'].append(logistic.score(X_test,y_test))
     logistic_prediction = logistic.predict(X_test)
     logistic_scores['precision_scores'].append(precision_score(y_test,logistic_prediction))
     logistic_scores['recall_scores'].append(recall_score(y_test,logistic_prediction))

 print("Decision Tree")
 print("  accuracy:",np.mean(tree_scores['accuracy_scores']))
 print("  precision:",np.mean(tree_scores['precision_scores']))
 print("  recall:",np.mean(tree_scores['recall_scores']))
 print("Logistic Regression")
 print("  accuracy:",np.mean(logistic_scores['accuracy_scores']))
 print("  precision:",np.mean(logistic_scores['precision_scores']))
 print("  recall:",np.mean(logistic_scores['recall_scores']))

在for循环中注释的两行同时给出了和精度的错误，我不知道为什么。尽管在我同时运行两个精度n时，它们还是起作用了。而且我似乎也找不到任何拼写错误。

我想知道不同的Python语法是否与sklearn混淆？因为一旦我尝试过这样的组合：

X = df.loc['Plass':'fare'].values
y = df.Survived.values

它给出了错误，但是当我使用正常的预期方式时，它工作正常。

（注意：首次使用stackexchange的人可能缩进了代码。）

解决方法

DecisionTreeClassifier确实没有这种方法。

您需要更改：

tree_scores['precision_scores'].append(tree.precision_score(y_test,tree_prediction))
tree_scores['recall_scores'].append(tree.recall_score(y_test,tree_prediction))

收件人：

tree_scores['precision_scores'].append(precision_score(y_test,tree_prediction))
tree_scores['recall_scores'].append(recall_score(y_test,tree_prediction))

你没事