RFE
¶
-
class
ibex.sklearn.feature_selection.
RFE
(estimator, n_features_to_select=None, step=1, verbose=0)¶ Bases:
sklearn.feature_selection.rfe.RFE
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Feature ranking with recursive feature elimination.
Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and the importance of each feature is obtained either through a
coef_
attribute or through afeature_importances_
attribute. Then, the least important features are pruned from current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached.Read more in the User Guide.
- estimator : object
- A supervised learning estimator with a
fit
method that provides information about feature importance either through acoef_
attribute or through afeature_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 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.
- verbose : int, default=0
- Controls verbosity of output.
- n_features_ : int
- The number of selected features.
- 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. - estimator_ : object
- The external estimator fit on the reduced dataset.
The following example shows how to retrieve the 5 right informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1 >>> from sklearn.feature_selection import RFE >>> from sklearn.svm import SVR >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0) >>> estimator = SVR(kernel="linear") >>> selector = RFE(estimator, 5, step=1) >>> selector = selector.fit(X, y) >>> selector.support_ array([ True, True, True, True, True, False, False, False, False, False], dtype=bool) >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
[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. -
decision_function
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- A parameter
-
fit
(X, y)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- Fit the RFE model and then the underlying estimator on the selected
- features.
- X : {array-like, sparse matrix}, shape = [n_samples, n_features]
- The training input samples.
- y : array-like, shape = [n_samples]
- The target values.
- A parameter
-
fit_transform
(X, y=None, **fit_params)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- X : numpy array of shape [n_samples, n_features]
- Training set.
- y : numpy array of shape [n_samples]
- Target values.
- X_new : numpy array of shape [n_samples, n_features_new]
- Transformed array.
- A parameter
-
inverse_transform
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Reverse the transformation operation
- X : array of shape [n_samples, n_selected_features]
- The input samples.
- X_r : array of shape [n_samples, n_original_features]
- X with columns of zeros inserted where features would have been removed by transform.
- A parameter
-
predict
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- Reduce X to the selected features and then predict using the
- underlying estimator.
- X : array of shape [n_samples, n_features]
- The input samples.
- y : array of shape [n_samples]
- The predicted target values.
- A parameter
-
predict_log_proba
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- A parameter
-
predict_proba
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- A parameter
-
score
(X, y)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- Reduce X to the selected features and then return the score of the
- underlying estimator.
- X : array of shape [n_samples, n_features]
- The input samples.
- y : array of shape [n_samples]
- The target values.
- A parameter
-
transform
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Reduce X to the selected features.
- X : array of shape [n_samples, n_features]
- The input samples.
- X_r : array of shape [n_samples, n_selected_features]
- The input samples with only the selected features.
- A parameter
- A parameter