Recipe: How to find optimal parameters using RandomizedSearchCV?

How to find optimal parameters using RandomizedSearchCV?

This recipe helps you find optimal parameters using RandomizedSearchCV
In [1]:
def Snippet_196():
    print(format('How to find parameters using RandomizedSearchCV','*^82'))

    import warnings

    # 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 =; y =
    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), y_train)

    # Results from Random Search
    print(" Results from Random Search " )
    print("\n The best estimator across ALL searched params:\n",
    print("\n The best score across ALL searched params:\n",
    print("\n The best parameters across ALL searched params:\n",
    print("\n ========================================================")

*****************How to find parameters using RandomizedSearchCV******************
/Users/nilimesh/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/ 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.
 Results from Random Search

 The best estimator across ALL searched params:
 GradientBoostingClassifier(criterion='friedman_mse', init=None,
              learning_rate=0.02933763179021598, loss='deviance',
              max_depth=6, 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=973,
              n_iter_no_change=None, presort='auto', random_state=None,
              subsample=0.34643411696436155, tol=0.0001,
              validation_fraction=0.1, verbose=0, warm_start=False)

 The best score across ALL searched params:

 The best parameters across ALL searched params:
 {'learning_rate': 0.02933763179021598, 'max_depth': 6, 'n_estimators': 973, 'subsample': 0.34643411696436155}


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