DOC RFE docstring + link RFECV in narrative docs

larsmans · GaelVaroquaux · commit 890bb8e13aa3 · 2012-10-06T15:53:18.000+02:00
diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst
@@ -62,6 +62,9 @@ are the smallest are pruned from the current set features. That procedure is
 recursively repeated on the pruned set until the desired number of features to
 select is eventually reached.
 
+:class:`RFECV` performs RFE in a cross-validation loop to find the optimal
+number of features.
+
 .. topic:: Examples:
 
     * :ref:`example_plot_rfe_digits.py`: A recursive feature elimination example
diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py
@@ -107,10 +107,10 @@ def fit(self, X, y):
 
         Parameters
         ----------
-        X : array of shape [n_samples, n_features]
+        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
             The training input samples.
 
-        y : array of shape [n_samples]
+        y : array-like, shape = [n_samples]
             The target values.
         """
         X, y = check_arrays(X, y, sparse_format="csr")
@@ -319,11 +319,11 @@ def fit(self, X, y):
 
         Parameters
         ----------
-        X : array of shape [n_samples, n_features]
+        X : {array-like, sparse matrix}, 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 of shape [n_samples]
+        y : array-like, shape = [n_samples]
             Target values (integers for classification, real numbers for
             regression).
         """
