ens_maxvoting.py

#get the data sets
X_train, X_val, y_train, y_val = get_split_data(german_cred, target_name='bad_credit')

#fit single models
log_cf.fit(X_train, y_train)
knn_cf.fit(X_train, y_train)
svc_cf.fit(X_train, y_train)

#make predictions with trained models
pred1 = log_cf.predict(X_val)
pred2 = knn_cf.predict(X_val)
pred3 = svc_cf.predict(X_val)

#Take max voting as final prediction
maxpred = []

for i in range(0, len(X_val)):
    #calculate the mode and append to maxpred vector
    maxpred.append(mode([pred1[i], pred2[i], pred3[i]]))
    
    
print("Logistic Regression Model")
print(get_acc(pred1, y_val))
print("KNN Classifier Model")
print(get_acc(pred2, y_val))
print("SVR Classifier Model")
print(get_acc(pred3, y_val))

print("Max Voting Model")
print(get_acc(np.array(maxpred), y_val))