old_contents: import pandas as pd
  from sklearn.metrics import accuracy_score, log_loss
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC, LinearSVC, NuSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
  
  classifiers = [
      KNeighborsClassifier(3),
      SVC(kernel="rbf", C=0.025, probability=True),
      NuSVC(probability=True),
      DecisionTreeClassifier(),
      RandomForestClassifier(),
      AdaBoostClassifier(),
      GradientBoostingClassifier(),
      GaussianNB(),
      LinearDiscriminantAnalysis(),
      QuadraticDiscriminantAnalysis()]
  
  def classify_test(X_train,y_train,X_test,y_test):
      for clf in classifiers:
          clf.fit(X_train, y_train)
          name = clf.__class__.__name__
          print("="*30)
          print(name)
          print('****Results****')
          train_predictions = clf.predict(X_test)
          acc = accuracy_score(y_test, train_predictions)
          print("Accuracy: {:.4%}".format(acc))
          train_predictions = clf.predict_proba(X_test)
          ll = log_loss(y_test, train_predictions)
          print("Log Loss: {}".format(ll))
      print("="*30)
new_contents: import pandas as pd
  from sklearn.metrics import accuracy_score, log_loss
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC, LinearSVC, NuSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
  
  classifiers = [
      KNeighborsClassifier(3),
      SVC(kernel="rbf", C=0.025, probability=True),
      NuSVC(probability=True),
      DecisionTreeClassifier(),
      RandomForestClassifier(),
      AdaBoostClassifier(),
      GradientBoostingClassifier(),
      GaussianNB(),
      LinearDiscriminantAnalysis(),
      QuadraticDiscriminantAnalysis()]
  
  def classify_test(X_train,y_train,X_test,y_test):
      for clf in classifiers:
          try:
              clf.fit(X_train, y_train)
          except:
              print('{} is wrong'.format(clf.__class__.__name__))   
          else:     
              name = clf.__class__.__name__
              print("="*30)
              print(name)
              print('****Results****')
              train_predictions = clf.predict(X_test)
              acc = accuracy_score(y_test, train_predictions)
              print("Accuracy: {:.4%}".format(acc))
              train_predictions = clf.predict_proba(X_test)
              ll = log_loss(y_test, train_predictions)
              print("Log Loss: {}".format(ll))
      print("="*30)
subject: add try to choose aligram