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Elastic Net model with iterative fitting along a regularization path This node has been automatically generated by wrapping the ``sklearn.linear_model.coordinate_descent.ElasticNetCV`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. The best model is selected by crossvalidation. Read more in the :ref:`User Guide <elastic_net>`. **Parameters** l1_ratio : float or array of floats, optional float between 0 and 1 passed to ElasticNet (scaling between l1 and l2 penalties). For ``l1_ratio = 0`` the penalty is an L2 penalty. For ``l1_ratio = 1`` it is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a combination of L1 and L2 This parameter can be a list, in which case the different values are tested by crossvalidation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in ``[.1, .5, .7, .9, .95, .99, 1]`` eps : float, optional Length of the path. ``eps=1e3`` means that ``alpha_min / alpha_max = 1e3``. n_alphas : int, optional Number of alphas along the regularization path, used for each l1_ratio. alphas : numpy array, optional List of alphas where to compute the models. If None alphas are set automatically precompute : True  False  'auto'  arraylike Whether to use a precomputed Gram matrix to speed up calculations. If set to ``'auto'`` let us decide. The Gram matrix can also be passed as argument. max_iter : int, optional The maximum number of iterations tol : float, optional The tolerance for the optimization: if the updates are smaller than ``tol``, the optimization code checks the dual gap for optimality and continues until it is smaller than ``tol``. cv : int, crossvalidation generator or an iterable, optional Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,  integer, to specify the number of folds.  An object to be used as a crossvalidation generator.  An iterable yielding train/test splits. For integer/None inputs, :class:`KFold` is used. Refer :ref:`User Guide <cross_validation>` for the various crossvalidation strategies that can be used here. verbose : bool or integer Amount of verbosity. n_jobs : integer, optional Number of CPUs to use during the cross validation. If ``1``, use all the CPUs. positive : bool, optional When set to ``True``, forces the coefficients to be positive. selection : str, default 'cyclic' If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to 'random') often leads to significantly faster convergence especially when tol is higher than 1e4. random_state : int, RandomState instance, or None (default) The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to 'random'. fit_intercept : boolean whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False If ``True``, the regressors X will be normalized before regression. copy_X : boolean, optional, default True If ``True``, X will be copied; else, it may be overwritten. **Attributes** ``alpha_`` : float The amount of penalization chosen by cross validation ``l1_ratio_`` : float The compromise between l1 and l2 penalization chosen by cross validation ``coef_`` : array, shape (n_features,)  (n_targets, n_features) Parameter vector (w in the cost function formula), ``intercept_`` : float  array, shape (n_targets, n_features) Independent term in the decision function. ``mse_path_`` : array, shape (n_l1_ratio, n_alpha, n_folds) Mean square error for the test set on each fold, varying l1_ratio and alpha. ``alphas_`` : numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas) The grid of alphas used for fitting, for each l1_ratio. ``n_iter_`` : int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha. **Notes** See examples/linear_model/lasso_path_with_crossvalidation.py for an example. To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array. The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. More specifically, the optimization objective is:: 1 / (2 * n_samples) * y  Xw^2_2 + + alpha * l1_ratio * w_1 + 0.5 * alpha * (1  l1_ratio) * w^2_2 If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to:: a * L1 + b * L2 for:: alpha = a + b and l1_ratio = a / (a + b). See also enet_path ElasticNet














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_train_seq List of tuples: 

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supported_dtypes Supported dtypes 

Elastic Net model with iterative fitting along a regularization path This node has been automatically generated by wrapping the ``sklearn.linear_model.coordinate_descent.ElasticNetCV`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. The best model is selected by crossvalidation. Read more in the :ref:`User Guide <elastic_net>`. **Parameters** l1_ratio : float or array of floats, optional float between 0 and 1 passed to ElasticNet (scaling between l1 and l2 penalties). For ``l1_ratio = 0`` the penalty is an L2 penalty. For ``l1_ratio = 1`` it is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a combination of L1 and L2 This parameter can be a list, in which case the different values are tested by crossvalidation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in ``[.1, .5, .7, .9, .95, .99, 1]`` eps : float, optional Length of the path. ``eps=1e3`` means that ``alpha_min / alpha_max = 1e3``. n_alphas : int, optional Number of alphas along the regularization path, used for each l1_ratio. alphas : numpy array, optional List of alphas where to compute the models. If None alphas are set automatically precompute : True  False  'auto'  arraylike Whether to use a precomputed Gram matrix to speed up calculations. If set to ``'auto'`` let us decide. The Gram matrix can also be passed as argument. max_iter : int, optional The maximum number of iterations tol : float, optional The tolerance for the optimization: if the updates are smaller than ``tol``, the optimization code checks the dual gap for optimality and continues until it is smaller than ``tol``. cv : int, crossvalidation generator or an iterable, optional Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,  integer, to specify the number of folds.  An object to be used as a crossvalidation generator.  An iterable yielding train/test splits. For integer/None inputs, :class:`KFold` is used. Refer :ref:`User Guide <cross_validation>` for the various crossvalidation strategies that can be used here. verbose : bool or integer Amount of verbosity. n_jobs : integer, optional Number of CPUs to use during the cross validation. If ``1``, use all the CPUs. positive : bool, optional When set to ``True``, forces the coefficients to be positive. selection : str, default 'cyclic' If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to 'random') often leads to significantly faster convergence especially when tol is higher than 1e4. random_state : int, RandomState instance, or None (default) The seed of the pseudo random number generator that selects a random feature to update. Useful only when selection is set to 'random'. fit_intercept : boolean whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False If ``True``, the regressors X will be normalized before regression. copy_X : boolean, optional, default True If ``True``, X will be copied; else, it may be overwritten. **Attributes** ``alpha_`` : float The amount of penalization chosen by cross validation ``l1_ratio_`` : float The compromise between l1 and l2 penalization chosen by cross validation ``coef_`` : array, shape (n_features,)  (n_targets, n_features) Parameter vector (w in the cost function formula), ``intercept_`` : float  array, shape (n_targets, n_features) Independent term in the decision function. ``mse_path_`` : array, shape (n_l1_ratio, n_alpha, n_folds) Mean square error for the test set on each fold, varying l1_ratio and alpha. ``alphas_`` : numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas) The grid of alphas used for fitting, for each l1_ratio. ``n_iter_`` : int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha. **Notes** See examples/linear_model/lasso_path_with_crossvalidation.py for an example. To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array. The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. More specifically, the optimization objective is:: 1 / (2 * n_samples) * y  Xw^2_2 + + alpha * l1_ratio * w_1 + 0.5 * alpha * (1  l1_ratio) * w^2_2 If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to:: a * L1 + b * L2 for:: alpha = a + b and l1_ratio = a / (a + b). See also enet_path ElasticNet




Predict using the linear model This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.ElasticNetCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns



Fit linear model with coordinate descent This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.ElasticNetCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Fit is on grid of alphas and best alpha estimated by crossvalidation. Parameters

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