How to find optimal parameters using RandomizedSearchCV?

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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