package sklearn
Module Feature_selection.RFECVSource
Source
val create :
?step:[ `Int of int | `Float of float ] ->
?min_features_to_select:int ->
?cv:
[ `Int of int | `CrossValGenerator of Py.Object.t | `Ndarray of Ndarray.t ] ->
?scoring:[ `String of string | `Callable of Py.Object.t | `None ] ->
?verbose:int ->
?n_jobs:[ `Int of int | `None ] ->
estimator:Py.Object.t ->
unit ->
tFeature ranking with recursive feature elimination and cross-validated selection of the best number of features.
See glossary entry for :term:`cross-validation estimator`.
Read more in the :ref:`User Guide <rfe>`.
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. 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 5-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:: 0.22 ``cv`` default value of None changed from 3-fold to 5-fold.
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.
Attributes ---------- n_features_ : int The number of selected features with cross-validation.
support_ : array of shape n_features The mask of selected features.
ranking_ : array of shape n_features The feature ranking, such that `ranking_i` corresponds to the ranking position of the i-th feature. Selected (i.e., estimated best) features are assigned rank 1.
grid_scores_ : array of shape n_subsets_of_features The cross-validation scores such that ``grid_scores_i`` corresponds to the CV score of the i-th subset of features.
estimator_ : object The external estimator fit on the reduced dataset.
Notes ----- The size of ``grid_scores_`` is equal to ``ceil((n_features - min_features_to_select) / step) + 1``, where step is the number of features removed at each iteration.
Allows NaN/Inf in the input if the underlying estimator does as well.
Examples -------- The following example shows how to retrieve the a-priori not known 5 informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1 >>> from sklearn.feature_selection import RFECV >>> from sklearn.svm import SVR >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0) >>> estimator = SVR(kernel="linear") >>> selector = RFECV(estimator, step=1, cv=5) >>> selector = selector.fit(X, y) >>> selector.support_ array( True, True, True, True, True, False, False, False, False, False) >>> selector.ranking_ array(1, 1, 1, 1, 1, 6, 4, 3, 2, 5)
See also -------- RFE : Recursive feature elimination
References ----------
.. 1 Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection for cancer classification using support vector machines", Mach. Learn., 46(1-3), 389--422, 2002.
Source
val decision_function :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tCompute the decision function of ``X``.
Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Returns ------- score : array, shape = n_samples, n_classes or n_samples The decision function of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification produce an array of shape n_samples.
Source
val fit :
?groups:[ `Ndarray of Ndarray.t | `None ] ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
y:Ndarray.t ->
t ->
tFit the RFE model and automatically tune the number of selected features.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the total number of features.
y : array-like of shape (n_samples,) Target values (integers for classification, real numbers for regression).
groups : array-like of shape (n_samples,) or None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).
Source
val fit_transform :
?y:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.tFit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters ---------- X : numpy array of shape n_samples, n_features Training set.
y : numpy array of shape n_samples Target values.
**fit_params : dict Additional fit parameters.
Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Get a mask, or integer index, of the features selected
Parameters ---------- indices : boolean (default False) If True, the return value will be an array of integers, rather than a boolean mask.
Returns ------- support : array An index that selects the retained features from a feature vector. If `indices` is False, this is a boolean array of shape # input features, in which an element is True iff its corresponding feature is selected for retention. If `indices` is True, this is an integer array of shape # output features whose values are indices into the input feature vector.
Reverse the transformation operation
Parameters ---------- X : array of shape n_samples, n_selected_features The input samples.
Returns ------- X_r : array of shape n_samples, n_original_features `X` with columns of zeros inserted where features would have been removed by :meth:`transform`.
Reduce X to the selected features and then predict using the underlying estimator.
Parameters ---------- X : array of shape n_samples, n_features The input samples.
Returns ------- y : array of shape n_samples The predicted target values.
Predict class log-probabilities for X.
Parameters ---------- X : array of shape n_samples, n_features The input samples.
Returns ------- p : array of shape (n_samples, n_classes) The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`.
Source
val predict_proba :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict class probabilities for X.
Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Returns ------- p : array of shape (n_samples, n_classes) The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`.
Reduce X to the selected features and then return the score of the underlying estimator.
Parameters ---------- X : array of shape n_samples, n_features The input samples.
y : array of shape n_samples The target values.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Reduce X to the selected features.
Parameters ---------- X : array of shape n_samples, n_features The input samples.
Returns ------- X_r : array of shape n_samples, n_selected_features The input samples with only the selected features.
Attribute estimator_: see constructor for documentation
Pretty-print the object to a formatter.