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- from sklearn.datasets import make_friedman1
- from sklearn.feature_selection import RFECV
- from sklearn.model_selection import GridSearchCV
- from sklearn.svm import SVR
- X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
- param_grid = [{'estimator__C': [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]}]
- estimator = SVR(kernel="linear")
- selector = RFECV(estimator, step=1, cv=4)
- clf = GridSearchCV(selector, param_grid, cv=7)
- clf.fit(X, y)
- print(clf.best_estimator_.estimator_)
- print(clf.best_estimator_.grid_scores_)
- print(clf.best_estimator_.ranking_)
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