title: machine learning,sklearn kfold,where the different betweent clfs?
body: <p>I have used kfold to create 10 classifiers.
  Now I need to choose one classifier to predict.
  Is one of these classifiers better than the rest or are there no difference between the classifiers?</p>
  
  <pre><code>kf=KFold(10,True)
  sum_jing=0
  sum_recall=0
  for train_index,test_index in kf.split(x2):
      x_train,x_test=x2.loc[train_index],x2.loc[test_index]
      y_train,y_test=y2.loc[train_index],y2.loc[test_index]
      #clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0,class_weight="balanced")#balanced比较准0.93#0.7
      #clf = RandomForestClassifier(n_estimators=100, random_state=0,class_weight="balanced",max_depth=2,max_features="auto")#0.93#0.83
      clf= RandomForestClassifier(n_estimators=100, random_state=0,class_weight="balanced_subsample",max_depth=2,max_features="auto")#0.93#0.73-0.89
      clf.fit(x_train,y_train)
      #print("精度",clf.score(x_test,y_test))
      y_predict=clf.predict(x_test)
      sum_jing=sum_jing+clf.score(x_test,y_test)
      sum_recall=sum_recall+metrics.recall_score(y_test,y_predict,)
      #print("召回率",metrics.recall_score(y_test,y_predict))
  print(sum_jing/10)
  print(sum_recall/10)
  from sklearn.externals import joblib
  import os
  os.chdir("chen")
  joblib.dump(clf, "train_model.m")
  </code></pre>