## How to do recursive feature elimination in Python
def Snippet_128():
    print()
    print(format('How to do recursive feature elimination in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn.datasets import make_regression
    from sklearn.feature_selection import RFECV
    from sklearn import linear_model

    # Create Data
    # Generate features matrix, target vector, and the true coefficients
    X, y = make_regression(n_samples = 10000, n_features = 100, n_informative = 2)
    print(); print(X.shape)

    # Create Linear Model
    ols = linear_model.LinearRegression()

    # Create recursive feature eliminator that scores features by mean squared errors
    rfecv = RFECV(estimator=ols, step=1, scoring='neg_mean_squared_error', cv=4, verbose=0,
                  n_jobs=4)

    # Fit recursive feature eliminator 
    rfecv.fit(X, y)

    # Recursive feature elimination
    rfecv.transform(X)

    # Number of best features
    print(); print(rfecv)
    print(); print(rfecv.n_features_)

Snippet_128()