How to find optimal parameters using GridSearchCV in Python

In [2]:
def Snippet_195(): 
    print()
    print(format('How to find parameters using GridSearchCV','*^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 sklearn.ensemble import GradientBoostingClassifier

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

    model = GradientBoostingClassifier()
    parameters = {'learning_rate': [0.01,0.02,0.03],
                  'subsample'    : [0.9, 0.5, 0.2],
                  'n_estimators' : [100,500,1000],
                  'max_depth'    : [4,6,8] 
                 }
    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_195()

********************How to find parameters using GridSearchCV*********************

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

 The best estimator across ALL searched params:
 GradientBoostingClassifier(criterion='friedman_mse', init=None,
              learning_rate=0.02, loss='deviance', max_depth=4,
              max_features=None, max_leaf_nodes=None,
              min_impurity_decrease=0.0, min_impurity_split=None,
              min_samples_leaf=1, min_samples_split=2,
              min_weight_fraction_leaf=0.0, n_estimators=100,
              n_iter_no_change=None, presort='auto', random_state=None,
              subsample=0.5, tol=0.0001, validation_fraction=0.1,
              verbose=0, warm_start=False)

 The best score across ALL searched params:
 0.9924812030075187

 The best parameters across ALL searched params:
 {'learning_rate': 0.02, 'max_depth': 4, 'n_estimators': 100, 'subsample': 0.5}

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