How to select model using Grid Search in Python?
MODEL SELECTION

How to select model using Grid Search in Python?

How to select model using Grid Search in Python?

This recipe helps you select model using Grid Search in Python

0
In [1]:
## How to select model using Grid Search in Python
def Snippet_144():
    print()
    print(format('How to select model using Grid Search in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    import numpy as np
    from sklearn import datasets
    from sklearn.linear_model import LogisticRegression
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import GridSearchCV
    from sklearn.pipeline import Pipeline

    # Set random seed
    np.random.seed(0)

    # Load Iris Dataset
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    # Create Pipeline With Model Selection Search Space
    pipe = Pipeline([('classifier', RandomForestClassifier())])

    # Create space of candidate learning algorithms and their hyperparameters
    search_space = [{'classifier': [LogisticRegression()],
                                   'classifier__penalty': ['l1', 'l2'],
                                   'classifier__C': np.logspace(0, 4, 10)
                    },
                    {'classifier': [RandomForestClassifier()],
                                   'classifier__n_estimators': [10, 100, 1000],
                                   'classifier__max_features': [1, 2, 3]
                    }]

    # Create grid search 
    clf = GridSearchCV(pipe, search_space, cv=5, verbose=0, n_jobs = -1)

    # Conduct Model Selection Using Grid Search
    best_model = clf.fit(X, y)

    # View best model
    print(); print(best_model.best_estimator_.get_params()['classifier'])

Snippet_144()
*****************How to select model using Grid Search in Python******************

LogisticRegression(C=7.742636826811269, class_weight=None, dual=False,
          fit_intercept=True, intercept_scaling=1, max_iter=100,
          multi_class='warn', n_jobs=None, penalty='l1', random_state=None,
          solver='warn', tol=0.0001, verbose=0, warm_start=False)

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