Cross_validation_score

Cross_validation_score.py

# Defining the models

models = [KNeighborsClassifier(n_neighbors = 3),
          MLPClassifier(alpha = 1),
          DecisionTreeClassifier(max_depth = 5),
          RandomForestClassifier(max_depth=5,n_estimators=10,max_features=1),
          AdaBoostClassifier(),
          GaussianNB(),
          SVC(kernel = 'linear'),
          SVC(kernel = 'rbf'),
          SVC(kernel = 'sigmoid')]

# KFold cross validation technique is used

validation_type = model_selection.KFold(n_splits = 10)

# Calculation of cross validation scores

cv_result = []
for i in range(len(models)):
  cv_result.append(model_selection.cross_val_score(models[i],X_train,y_train,cv = validation_type,scoring = 'accuracy').mean())