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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()
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