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Randomized Logistic Regression This node has been automatically generated by wrapping the ``sklearn.linear_model.randomized_l1.RandomizedLogisticRegression`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Randomized Regression works by resampling the train data and computing a LogisticRegression 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** C : float, optional, default=1 The regularization parameter C in the LogisticRegression. scaling : float, optional, default=0.5 The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. sample_fraction : float, optional, default=0.75 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, default=200 Number of randomized models. selection_threshold : float, optional, default=0.25 The score above which features should be selected. fit_intercept : boolean, optional, default=True 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. tol : float, optional, default=1e3 tolerance for stopping criteria of LogisticRegression 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 RandomizedLogisticRegression >>> randomized_logistic = RandomizedLogisticRegression() **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 RandomizedLasso, Lasso, ElasticNet














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_train_seq List of tuples: 

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supported_dtypes Supported dtypes 

Randomized Logistic Regression This node has been automatically generated by wrapping the ``sklearn.linear_model.randomized_l1.RandomizedLogisticRegression`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Randomized Regression works by resampling the train data and computing a LogisticRegression 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** C : float, optional, default=1 The regularization parameter C in the LogisticRegression. scaling : float, optional, default=0.5 The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. sample_fraction : float, optional, default=0.75 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, default=200 Number of randomized models. selection_threshold : float, optional, default=0.25 The score above which features should be selected. fit_intercept : boolean, optional, default=True 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. tol : float, optional, default=1e3 tolerance for stopping criteria of LogisticRegression 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 RandomizedLogisticRegression >>> randomized_logistic = RandomizedLogisticRegression() **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 RandomizedLasso, Lasso, ElasticNet







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

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