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Randomized Lasso. This node has been automatically generated by wrapping the ``sklearn.linear_model.randomized_l1.RandomizedLasso`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Randomized Lasso works by resampling the train data and computing a Lasso on each resampling. In short, the features selected more often are good features. It is also known as stability selection. Read more in the :ref:`User Guide <randomized_l1>`. **Parameters** alpha : float, 'aic', or 'bic', optional The regularization parameter alpha parameter in the Lasso. Warning: this is not the alpha parameter in the stability selection article which is scaling. scaling : float, optional The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. sample_fraction : float, optional The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used. n_resampling : int, optional Number of randomized models. selection_threshold: float, optional The score above which features should be selected. fit_intercept : boolean, optional 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). verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default True If True, the regressors X will be normalized before regression. precompute : True  False  'auto' 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 in the Lars algorithm. 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. n_jobs : integer, optional Number of CPUs to use during the resampling. If '1', use all the CPUs random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:  None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs  An int, giving the exact number of total jobs that are spawned  A string, giving an expression as a function of n_jobs, as in '2*n_jobs' memory : Instance of joblib.Memory or string Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory. **Attributes** ``scores_`` : array, shape = [n_features] Feature scores between 0 and 1. ``all_scores_`` : array, shape = [n_features, n_reg_parameter] Feature scores between 0 and 1 for all values of the regularization parameter. The reference article suggests ``scores_`` is the max of ``all_scores_``. **Examples** >>> from sklearn.linear_model import RandomizedLasso >>> randomized_lasso = RandomizedLasso() **Notes** See examples/linear_model/plot_sparse_recovery.py for an example. **References** Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417473, September 2010 DOI: 10.1111/j.14679868.2010.00740.x See also RandomizedLogisticRegression, LogisticRegression














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_train_seq List of tuples: 

dtype dtype 

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output_dim Output dimensions 

supported_dtypes Supported dtypes 

Randomized Lasso. This node has been automatically generated by wrapping the ``sklearn.linear_model.randomized_l1.RandomizedLasso`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Randomized Lasso works by resampling the train data and computing a Lasso on each resampling. In short, the features selected more often are good features. It is also known as stability selection. Read more in the :ref:`User Guide <randomized_l1>`. **Parameters** alpha : float, 'aic', or 'bic', optional The regularization parameter alpha parameter in the Lasso. Warning: this is not the alpha parameter in the stability selection article which is scaling. scaling : float, optional The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. sample_fraction : float, optional The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used. n_resampling : int, optional Number of randomized models. selection_threshold: float, optional The score above which features should be selected. fit_intercept : boolean, optional 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). verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default True If True, the regressors X will be normalized before regression. precompute : True  False  'auto' 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 in the Lars algorithm. 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. n_jobs : integer, optional Number of CPUs to use during the resampling. If '1', use all the CPUs random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:  None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs  An int, giving the exact number of total jobs that are spawned  A string, giving an expression as a function of n_jobs, as in '2*n_jobs' memory : Instance of joblib.Memory or string Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory. **Attributes** ``scores_`` : array, shape = [n_features] Feature scores between 0 and 1. ``all_scores_`` : array, shape = [n_features, n_reg_parameter] Feature scores between 0 and 1 for all values of the regularization parameter. The reference article suggests ``scores_`` is the max of ``all_scores_``. **Examples** >>> from sklearn.linear_model import RandomizedLasso >>> randomized_lasso = RandomizedLasso() **Notes** See examples/linear_model/plot_sparse_recovery.py for an example. **References** Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417473, September 2010 DOI: 10.1111/j.14679868.2010.00740.x See also RandomizedLogisticRegression, LogisticRegression







Fit the model using X, y as training data. This node has been automatically generated by wrapping the sklearn.linear_model.randomized_l1.RandomizedLasso class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

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