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Bayesian_Opt_Catboost = CatBoostClassifier()
Bayesian_Opt_Catboost.load_model('D:\optimizing-catboost-model-with-bayesian\saved_models\Models\Iteration-40\Bayesian_Opt_Catboost_10_40.cbm')


# After fitting your model on the training data
y_train_pred = Bayesian_Opt_Catboost.predict(X_train)

# Calculate training accuracy
train_accuracy = accuracy_score(y_train, y_train_pred)
print(f"Training Accuracy: {train_accuracy:.4f}")

# You can also get detailed metrics for the training set
print("\nTraining Classification Report:")
print(classification_report(y_train, y_train_pred))

# Plot confusion matrix for training data
plt.figure(figsize=(8, 6))
train_cm = confusion_matrix(y_train, y_train_pred)
sns.heatmap(train_cm, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Training Confusion Matrix')
plt.show()