> The distributions in `scipy.stats` prior to version scipy 0.16 do not allow specifying a random state. Instead, they use the global numpy random state, that can be seeded via `np.random.seed` or set using `np.random.set_state`. However, beginning scikit-learn 0.18, the [sklearn.model_selection](http://sklearn.apachecn.org/cn/0.19.0/modules/classes.html#module-sklearn.model_selection) module sets the random state provided by the user if scipy >= 0.16 is also available.
| [`linear_model.ElasticNetCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV"sklearn.linear_model.ElasticNetCV")([l1_ratio, eps, …]) | Elastic Net model with iterative fitting along a regularization path |
| [`linear_model.LarsCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LarsCV.html#sklearn.linear_model.LarsCV"sklearn.linear_model.LarsCV")([fit_intercept, …]) | Cross-validated Least Angle Regression model |
| [`linear_model.LassoCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV"sklearn.linear_model.LassoCV")([eps, n_alphas, …]) | Lasso linear model with iterative fitting along a regularization path |
| [`linear_model.LassoLarsCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsCV.html#sklearn.linear_model.LassoLarsCV"sklearn.linear_model.LassoLarsCV")([fit_intercept, …]) | Cross-validated Lasso, using the LARS algorithm |
| [`linear_model.LassoLarsIC`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html#sklearn.linear_model.LassoLarsIC"sklearn.linear_model.LassoLarsIC")([criterion, …]) | Lasso model fit with Lars using BIC or AIC for model selection |
| [`ensemble.RandomForestClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier"sklearn.ensemble.RandomForestClassifier")([…]) | A random forest classifier. |
| [`ensemble.RandomForestRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor"sklearn.ensemble.RandomForestRegressor")([…]) | A random forest regressor. |
| [`ensemble.ExtraTreesClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier"sklearn.ensemble.ExtraTreesClassifier")([…]) | An extra-trees classifier. |
| [`ensemble.ExtraTreesRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor"sklearn.ensemble.ExtraTreesRegressor")([n_estimators, …]) | An extra-trees regressor. |
| [`ensemble.GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier"sklearn.ensemble.GradientBoostingClassifier")([loss, …]) | Gradient Boosting for classification. |
| [`ensemble.GradientBoostingRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor"sklearn.ensemble.GradientBoostingRegressor")([loss, …]) | Gradient Boosting for regression. |