相关疑难解决方法(0)

我正在尝试使用sklearn评估多个机器学习算法,以获得几个指标(准确度,召回率,精度等等).

对于我从这里的文档和源代码(我使用sklearn 0.17)中理解的内容,cross_val_score函数只为每次执行接收一个记分器.因此,为了计算多个分数,我必须:

1. 执行多次

2. 实施我的(耗时且容易出错的)得分手

   我用这段代码执行了多次:

   from sklearn.svm import SVC
   from sklearn.naive_bayes import GaussianNB
   from sklearn.tree import DecisionTreeClassifier
   from sklearn.cross_validation import  cross_val_score
   import time
   from sklearn.datasets import  load_iris
   
   iris = load_iris()
   
   models = [GaussianNB(), DecisionTreeClassifier(), SVC()]
   names = ["Naive Bayes", "Decision Tree", "SVM"]
   for model, name in zip(models, names):
       print name
       start = time.time()
       for score in ["accuracy", "precision", "recall"]:
           print score,
           print " : ",
           print cross_val_score(model, iris.data, iris.target,scoring=score, cv=10).mean()
       print time.time() - start
   

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我得到这个输出: …