[source]

DyRFE

galaxy_ml.feature_selectors.DyRFE(estimator, n_features_to_select=None, step=1, verbose=0)

Mainly used with DyRFECV

Parameters

  • estimator: object
    A supervised learning estimator with a fit method that provides information about feature importance either through a coef_ attribute or through a feature_importances_ attribute.
  • n_features_to_select: int or None (default=None)
    The number of features to select. If None, half of the features are selected.
  • step: int, float or list, optional (default=1)
    If greater than or equal to 1, then step corresponds to the (integer) number of features to remove at each iteration. If within (0.0, 1.0), then step corresponds to the percentage (rounded down) of features to remove at each iteration. If list, a series of steps of features to remove at each iteration. Iterations stops when steps finish
  • verbose: int, (default=0)
    Controls verbosity of output.

[source]

DyRFECV

galaxy_ml.feature_selectors.DyRFECV(estimator, step=1, min_features_to_select=1, cv='warn', scoring=None, verbose=0, n_jobs=None)

Compared with RFECV, DyRFECV offers flexiable step to eleminate features, in the format of list, while RFECV supports only fixed number of step.

Parameters

  • estimator: object
    A supervised learning estimator with a fit method that provides information about feature importance either through a coef_ attribute or through a feature_importances_ attribute.
  • step: int or float, optional (default=1)
    If greater than or equal to 1, then step corresponds to the (integer) number of features to remove at each iteration. If within (0.0, 1.0), then step corresponds to the percentage (rounded down) of features to remove at each iteration. If list, a series of step to remove at each iteration. iteration stopes when finishing all steps Note that the last iteration may remove fewer than step features in order to reach min_features_to_select.
  • min_features_to_select: int, (default=1)
    The minimum number of features to be selected. This number of features will always be scored, even if the difference between the original feature count and min_features_to_select isn't divisible by step.

  • cv: int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - :term:CV splitter, - An iterable yielding (train, test) splits as arrays of indices.

    For integer/None inputs, if ``y`` is binary or multiclass,
:class:`sklearn.model_selection.StratifiedKFold` is used. If the
estimator is a classifier or if ``y`` is neither binary nor multiclass,
:class:`sklearn.model_selection.KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
.. versionchanged:: 0.20
    ``cv`` default value of None will change from 3-fold to 5-fold
    in v0.22.

  • scoring: string, callable or None, optional, (default=None)
    A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

  • verbose: int, (default=0)
    Controls verbosity of output.
  • n_jobs: int or None, optional (default=None)
    Number of cores to run in parallel while fitting across folds. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.