How to find optimal parameters using RandomizedSearchCV

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def Snippet_196(): 
    print()
    print(format('How to find parameters using RandomizedSearchCV','*^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 RandomizedSearchCV
    from sklearn.ensemble import GradientBoostingClassifier
    from scipy.stats import uniform as sp_randFloat
    from scipy.stats import randint as sp_randInt    

    # 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': sp_randFloat(),
                  'subsample'    : sp_randFloat(),
                  'n_estimators' : sp_randInt(100, 1000),
                  'max_depth'    : sp_randInt(4, 10) 
                 }
    
    randm = RandomizedSearchCV(estimator=model, param_distributions = parameters, 
                               cv = 2, n_iter = 10, n_jobs=-1)
    randm.fit(X_train, y_train)

    # Results from Random Search
    print("\n========================================================")
    print(" Results from Random Search " )
    print("========================================================")    
    
    print("\n The best estimator across ALL searched params:\n",
          randm.best_estimator_)
    
    print("\n The best score across ALL searched params:\n",
          randm.best_score_)
    
    print("\n The best parameters across ALL searched params:\n",
          randm.best_params_)
    
    print("\n ========================================================")
Snippet_196()

*****************How to find parameters using RandomizedSearchCV******************

========================================================
 Results from Random Search 
========================================================

 The best estimator across ALL searched params:
 GradientBoostingClassifier(criterion='friedman_mse', init=None,
              learning_rate=0.7965797501631773, 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=946,
              n_iter_no_change=None, presort='auto', random_state=None,
              subsample=0.3640903065065887, tol=0.0001,
              validation_fraction=0.1, verbose=0, warm_start=False)

 The best score across ALL searched params:
 0.9774436090225563

 The best parameters across ALL searched params:
 {'learning_rate': 0.7965797501631773, 'max_depth': 4, 'n_estimators': 946, 'subsample': 0.3640903065065887}

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