Predictive Modeling의 cross validation

Cross validation

from sklearn.model_selection import KFold #for K-fold cross validation
from sklearn.model_selection import cross_val_score # score evaluation
from sklearn.model_selection import cross_val_predict # prediction

kfold = KFold(n_splits=10, random_state=None) 
xyz=[]
accuracy=[]
std=[]
classifiers=['Linear Svm', 'Radial Svm','Logistic Regression','KNN','Decision Tree','Naive Bayes','Random Forest']
models=[svm.SVC(kernel='linear'),svm.SVC(kernel='rbf'),LogisticRegression(),KNeighborsClassifier(n_neighbors=9),DecisionTreeClassifier(),GaussianNB(),RandomForestClassifier(n_estimators=100)]
for i in models:
    model = i
    cv_result = cross_val_score(model,X,Y, cv = kfold,scoring="accuracy")
    cv_result = cv_result
    xyz.append(cv_result.mean())
    std.append(cv_result.std())
    accuracy.append(cv_result)
new_models_dataframe2=pd.DataFrame({'CV Mean':xyz, 'Std':std},index=classifiers)
new_models_dataframe2

plt.subplots(figsize=(12,6))
box=pd.DataFrame(accuracy,index=[classifiers])
box.T.boxplot()

new_models_dataframe2['CV Mean'].plot.barh(width=0.8)
plt.title('Average CV Mean Accuracy')
fig=plt.gcf()
fig.set_size_inches(8,5)
plt.show()