d4f36a7624c780f2d3cd38aaf7c6ee61f84739c8,examples/catboost_simple.py,,objective,#Any#,30

Before Change

        "boosting_type": trial.suggest_categorical("boosting_type", ["Ordered", "Plain"])
    }

    if param["boosting_type"] == "Ordered":
        param["learning_rate"] = trial.suggest_loguniform("learning_rate", 1e-4, 0.1)
    if param["boosting_type"] == "Plain":
        param["learning_rate"] = trial.suggest_loguniform("learning_rate", 1e-6, 1e-2)

    gbm = cb.CatBoostClassifier(**param)

After Change

    }

    if param["bootstrap_type"] == "Bayesian":
        param["bagging_temperature"] = trial.suggest_uniform("bagging_temperature", 0, 10)
    elif param["bootstrap_type"] == "Bernoulli":
       param["subsample"] = trial.suggest_uniform("subsample", 0, 1)
    elif param["bootstrap_type"] == "MVS":
       param["mvs_head_fraction"] = trial.suggest_uniform("mvs_head_fraction", 0, 1)

    gbm = cb.CatBoostClassifier(**param)

    gbm.fit(train_x, train_y, eval_set=[(test_x, test_y)], verbose=0, early_stopping_rounds=100)

Instances