In [1]:

def Snippet_199():
    print()
    print(format('How to find optimal parameters for CatBoost using GridSearchCV for Regression','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import GridSearchCV
    from catboost import CatBoostRegressor

    # load the iris datasets
    dataset = datasets.load_boston()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    model = CatBoostRegressor()
    parameters = {'depth'         : [6,8,10],
                  'learning_rate' : [0.01, 0.05, 0.1],
                  'iterations'    : [30, 50, 100]
                 }
    grid = GridSearchCV(estimator=model, param_grid = parameters, cv = 2, n_jobs=-1)
    grid.fit(X_train, y_train)

    # Results from Grid Search
    print("\n========================================================")
    print(" Results from Grid Search " )
    print("========================================================")
    print("\n The best estimator across ALL searched params:\n",
          grid.best_estimator_)
    print("\n The best score across ALL searched params:\n",
          grid.best_score_)
    print("\n The best parameters across ALL searched params:\n",
          grid.best_params_)
    print("\n ========================================================")

Snippet_199()

**How to find optimal parameters for CatBoost using GridSearchCV for Regression***

/Users/nilimesh/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.
  DeprecationWarning)

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========================================================
 Results from Grid Search
========================================================

 The best estimator across ALL searched params:
 <catboost.core.CatBoostRegressor object at 0x1a2302af60>

 The best score across ALL searched params:
 20.27497626782402

 The best parameters across ALL searched params:
 {'depth': 10, 'iterations': 30, 'learning_rate': 0.01}

 ========================================================

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