Answer to: XGBoost Pipeline with ColumnTransformer: Handling categorical + numerical data and improving accuracy

You can use grid search to improve from sklearn.model_selection import GridSearchCV # Define hyperparameter grid param_grid = { 'classifier__n_estimators': [100, 200], 'classifier__max_depth': [3, 5, 7], 'classifier__learning_rate': [0.01, 0.1], 'classifier__subsample': [0.8, 1.0], 'classifier__colsample_bytree': [0.8, 1.0] } # Initialize GridSearchCV grid_search = GridSearchCV( estimator=model, # your existing pipeline param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1 ) # Fit grid search on training data print("Running Grid Search...") grid_search.fit(X_train, y_train) # Get best model best_model = grid_search.best_estimator_ print("Best Parameters:", grid_search.best_params_)