Kernel_SVM.py

# hyperparameter tuning the XGBoost model
params ={'C':[10 ** x for x in range(-5, 3)],'gamma':[10 ** x for x in range(-5, 3)]}
# Create a custom (MCC) metric for evaluation of the model performance while 

mcc = make_scorer(matthews_corrcoef, greater_is_better=True)

# Create an XGBoost classifier object with log-loss as the loss function to minimize
svm_clf = svm.SVC(random_state=42,kernel='rbf',class_weight='balanced')


# Perform stratified 5-fold cross validation
grid_clf = GridSearchCV(svm_clf, params, scoring=mcc, cv=5, return_train_score=True)

# Fit the model
grid_clf .fit(X_train, y_train)
print(f"Best CV score: {grid_clf.best_score_}")

ok