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Linear regression with combined L1 and L2 priors as regularizer. This node has been automatically generated by wrapping the ``sklearn.linear_model.coordinate_descent.ElasticNet`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Minimizes the objective function:: 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 where:: alpha = a + b and l1_ratio = a / (a + b) The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio = 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable, unless you supply your own sequence of alpha. Read more in the :ref:`User Guide <elastic_net>`. **Parameters** alpha : float Constant that multiplies the penalty terms. Defaults to 1.0 See the notes for the exact mathematical meaning of this parameter. ``alpha = 0`` is equivalent to an ordinary least square, solved by the :class:`LinearRegression` object. For numerical reasons, using ``alpha = 0`` with the Lasso object is not advised and you should prefer the LinearRegression object. l1_ratio : float The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. 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. fit_intercept : bool Whether the intercept should be estimated or not. If ``False``, the data is assumed to be already centered. normalize : boolean, optional, default False If ``True``, the regressors X will be normalized before regression. 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. For sparse input this option is always ``True`` to preserve sparsity. WARNING : The ``'auto'`` option is deprecated and will be removed in 0.18. max_iter : int, optional The maximum number of iterations copy_X : boolean, optional, default True If ``True``, X will be copied; else, it may be overwritten. 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``. warm_start : bool, optional When set to ``True``, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. 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'. **Attributes** ``coef_`` : array, shape (n_features,)  (n_targets, n_features) parameter vector (w in the cost function formula) ``sparse_coef_`` : scipy.sparse matrix, shape (n_features, 1)  (n_targets, n_features) ``sparse_coef_`` is a readonly property derived from ``coef_`` ``intercept_`` : float  array, shape (n_targets,) independent term in decision function. ``n_iter_`` : arraylike, shape (n_targets,) number of iterations run by the coordinate descent solver to reach the specified tolerance. **Notes** To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array. See also SGDRegressor: implements elastic net regression with incremental training. SGDClassifier: implements logistic regression with elastic net penalty (``SGDClassifier(loss="log", penalty="elasticnet")``).














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_train_seq List of tuples: 

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supported_dtypes Supported dtypes 

Linear regression with combined L1 and L2 priors as regularizer. This node has been automatically generated by wrapping the ``sklearn.linear_model.coordinate_descent.ElasticNet`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Minimizes the objective function:: 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 where:: alpha = a + b and l1_ratio = a / (a + b) The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio = 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable, unless you supply your own sequence of alpha. Read more in the :ref:`User Guide <elastic_net>`. **Parameters** alpha : float Constant that multiplies the penalty terms. Defaults to 1.0 See the notes for the exact mathematical meaning of this parameter. ``alpha = 0`` is equivalent to an ordinary least square, solved by the :class:`LinearRegression` object. For numerical reasons, using ``alpha = 0`` with the Lasso object is not advised and you should prefer the LinearRegression object. l1_ratio : float The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. 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. fit_intercept : bool Whether the intercept should be estimated or not. If ``False``, the data is assumed to be already centered. normalize : boolean, optional, default False If ``True``, the regressors X will be normalized before regression. 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. For sparse input this option is always ``True`` to preserve sparsity. WARNING : The ``'auto'`` option is deprecated and will be removed in 0.18. max_iter : int, optional The maximum number of iterations copy_X : boolean, optional, default True If ``True``, X will be copied; else, it may be overwritten. 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``. warm_start : bool, optional When set to ``True``, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. 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'. **Attributes** ``coef_`` : array, shape (n_features,)  (n_targets, n_features) parameter vector (w in the cost function formula) ``sparse_coef_`` : scipy.sparse matrix, shape (n_features, 1)  (n_targets, n_features) ``sparse_coef_`` is a readonly property derived from ``coef_`` ``intercept_`` : float  array, shape (n_targets,) independent term in decision function. ``n_iter_`` : arraylike, shape (n_targets,) number of iterations run by the coordinate descent solver to reach the specified tolerance. **Notes** To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortrancontiguous numpy array. See also SGDRegressor: implements elastic net regression with incremental training. SGDClassifier: implements logistic regression with elastic net penalty (``SGDClassifier(loss="log", penalty="elasticnet")``).




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



Fit model with coordinate descent. This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.ElasticNet class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Notes Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortrancontiguous numpy array if necessary. To avoid memory reallocation it is advised to allocate the initial data in memory directly using that format.

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