sk_feature_process.py 文件源码

def rfr_feature_select():
    from sklearn.datasets import load_boston
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.cross_validation import cross_val_score, ShuffleSplit

    boston = load_boston()
    X = boston["data"]
    Y = boston["target"]
    names = boston["feature_names"]

    rf = RandomForestRegressor(n_estimators=20, max_depth=4)
    scores = []
    for i in range(X.shape[1]):
        score = cross_val_score(rf, X[:, i:i + 1],
                                Y, scoring="r2", cv=ShuffleSplit(len(X), 3, .3))
        scores.append((round(np.mean(score), 3), names[i]))

    print sorted(scores, reverse=True)