automl-patternslisted

# AutoML & Hyperparameter Optimization Patterns ## Optuna ```python import optuna from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import cross_val_score def objective(trial: optuna.Trial) -> float: params = { "n_estimators": trial.suggest_int("n_estimators", 100, 1000, step=100), "learning_rate": trial.suggest_float("learning_rate", 1e-3, 0.3, log=True), "max_depth": trial.suggest_int("max_depth", 3, 9), "subsample": trial.suggest_float("subsample", 0.5, 1.0), "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 20), } model = GradientBoostingClassifier(**params, random_state=42) scores = cross_val_score(model, X_train, y_train, cv=5, scoring="roc_auc", n_jobs=-1) return scores.mean() study = optuna.create_study( direction="maximize", sampler=optuna.samplers.TPESampler(seed=42), pruner=optuna.pruners.MedianPruner(n_warmup_steps=5), storage="sqlite:///optuna.db", study_name="gbm_search", load_if_exists=True, ) study.optimize(objective, n_trials=100, timeout=3600, n_jobs=2) print("Best params:", study.best_params) print("Best AUC:", study.best_value) ``` ## Optuna with Pruning (for Iterative Models) ```python import lightgbm as lgb def objective(trial): params = { "num_leaves": trial.suggest_int("num_leaves", 20, 300), "learning_rate": trial.suggest_float("lr", 1e-3, 0.1, log=True), "min_child_samples": trial.sugges