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Lasso model fit with Least Angle Regression a.k.a. Lars This node has been automatically generated by wrapping the ``sklearn.linear_model.least_angle.LassoLars`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. It is a Linear Model trained with an L1 prior as regularizer. The optimization objective for Lasso is:: (1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1 Read more in the :ref:`User Guide <least_angle_regression>`. **Parameters** alpha : float Constant that multiplies the penalty term. Defaults to 1.0. ``alpha = 0`` is equivalent to an ordinary least square, solved by :class:`LinearRegression`. For numerical reasons, using ``alpha = 0`` with the LassoLars object is not advised and you should prefer the LinearRegression object. 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). positive : boolean (default=False) Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. Under the positive restriction the model coefficients will not converge to the ordinaryleastsquares solution for small values of alpha. Only coeffiencts up to the smallest alpha value (``alphas_[alphas_ > 0.].min()`` when fit_path=True) reached by the stepwise LarsLasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. verbose : boolean or integer, optional Sets the verbosity amount 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. 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 : integer, optional Maximum number of iterations to perform. eps : float, optional The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems. Unlike the ``tol`` parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization. fit_path : boolean If ``True`` the full path is stored in the ``coef_path_`` attribute. If you compute the solution for a large problem or many targets, setting ``fit_path`` to ``False`` will lead to a speedup, especially with a small alpha. **Attributes** ``alphas_`` : array, shape (n_alphas + 1,)  list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration. ``n_alphas`` is either ``max_iter``, ``n_features``, or the number of nodes in the path with correlation greater than ``alpha``, whichever is smaller. ``active_`` : list, length = n_alphas  list of n_targets such lists Indices of active variables at the end of the path. ``coef_path_`` : array, shape (n_features, n_alphas + 1) or list If a list is passed it's expected to be one of n_targets such arrays. The varying values of the coefficients along the path. It is not present if the ``fit_path`` parameter is ``False``. ``coef_`` : array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula). ``intercept_`` : float  array, shape (n_targets,) Independent term in decision function. ``n_iter_`` : arraylike or int. The number of iterations taken by lars_path to find the grid of alphas for each target. **Examples** >>> from sklearn import linear_model >>> clf = linear_model.LassoLars(alpha=0.01) >>> clf.fit([[1, 1], [0, 0], [1, 1]], [1, 0, 1]) ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True, fit_path=True, max_iter=500, normalize=True, positive=False, precompute='auto', verbose=False) >>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE [ 0. 0.963257...] See also lars_path lasso_path Lasso LassoCV LassoLarsCV sklearn.decomposition.sparse_encode














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_train_seq List of tuples: 

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Lasso model fit with Least Angle Regression a.k.a. Lars This node has been automatically generated by wrapping the ``sklearn.linear_model.least_angle.LassoLars`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. It is a Linear Model trained with an L1 prior as regularizer. The optimization objective for Lasso is:: (1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1 Read more in the :ref:`User Guide <least_angle_regression>`. **Parameters** alpha : float Constant that multiplies the penalty term. Defaults to 1.0. ``alpha = 0`` is equivalent to an ordinary least square, solved by :class:`LinearRegression`. For numerical reasons, using ``alpha = 0`` with the LassoLars object is not advised and you should prefer the LinearRegression object. 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). positive : boolean (default=False) Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. Under the positive restriction the model coefficients will not converge to the ordinaryleastsquares solution for small values of alpha. Only coeffiencts up to the smallest alpha value (``alphas_[alphas_ > 0.].min()`` when fit_path=True) reached by the stepwise LarsLasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. verbose : boolean or integer, optional Sets the verbosity amount 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. 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 : integer, optional Maximum number of iterations to perform. eps : float, optional The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems. Unlike the ``tol`` parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization. fit_path : boolean If ``True`` the full path is stored in the ``coef_path_`` attribute. If you compute the solution for a large problem or many targets, setting ``fit_path`` to ``False`` will lead to a speedup, especially with a small alpha. **Attributes** ``alphas_`` : array, shape (n_alphas + 1,)  list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration. ``n_alphas`` is either ``max_iter``, ``n_features``, or the number of nodes in the path with correlation greater than ``alpha``, whichever is smaller. ``active_`` : list, length = n_alphas  list of n_targets such lists Indices of active variables at the end of the path. ``coef_path_`` : array, shape (n_features, n_alphas + 1) or list If a list is passed it's expected to be one of n_targets such arrays. The varying values of the coefficients along the path. It is not present if the ``fit_path`` parameter is ``False``. ``coef_`` : array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula). ``intercept_`` : float  array, shape (n_targets,) Independent term in decision function. ``n_iter_`` : arraylike or int. The number of iterations taken by lars_path to find the grid of alphas for each target. **Examples** >>> from sklearn import linear_model >>> clf = linear_model.LassoLars(alpha=0.01) >>> clf.fit([[1, 1], [0, 0], [1, 1]], [1, 0, 1]) ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True, fit_path=True, max_iter=500, normalize=True, positive=False, precompute='auto', verbose=False) >>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE [ 0. 0.963257...] See also lars_path lasso_path Lasso LassoCV LassoLarsCV sklearn.decomposition.sparse_encode




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



Fit the model using X, y as training data. This node has been automatically generated by wrapping the sklearn.linear_model.least_angle.LassoLars class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. parameters
returns

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