DOC Adds consistence in docs for univariate selection metrics
@@ -51,15 +51,15 @@ def f_oneway(*args):
Parameters
----------
- *args : array-like, sparse matrices
+ *args : {array-like, sparse matrix}
sample1, sample2... The sample measurements should be given as
arguments.
Returns
-------
- F-value : float
+ f_statistic : float
The computed F-value of the test.
- p-value : float
+ p_value : float
The associated p-value from the F-distribution.
Notes
@@ -127,19 +127,19 @@ def f_classif(X, y):
Parameters
----------
- X : {array-like, sparse matrix} shape = [n_samples, n_features]
+ X : {array-like, sparse matrix} of shape (n_samples, n_features)
The set of regressors that will be tested sequentially.
- y : array of shape(n_samples)
- The data matrix.
+ y : ndarray of shape (n_samples,)
+ The target vector.
Returns
-------
- F : array, shape = [n_features,]
- The set of F values.
+ f_statistic : ndarray of shape (n_features,)
+ F-statistic for each feature.
- pval : array, shape = [n_features,]
- The set of p-values.
+ p_values : ndarray of shape (n_features,)
+ P-values associated with the F-statistic.
See Also
--------
@@ -195,10 +195,11 @@ def chi2(X, y):
Returns
-------
- chi2 : array, shape = (n_features,)
- chi2 statistics of each feature.
- pval : array, shape = (n_features,)
- p-values of each feature.
+ chi2 : ndarray of shape (n_features,)
+ Chi2 statistics for each feature.
+
+ p_values : ndarray of shape (n_features,)
+ P-values for each feature.
Notes
-----
