[MRG+1] Misleading gamma description (scikit-learn#8699) (scikit-learn#8716)

[DOC] update description of gamma for kernel methods

Author: mppaskov

sklearn/cluster/spectral.py

@@ -311,9 +311,8 @@ class SpectralClustering(BaseEstimator, ClusterMixin):
         by the clustering algorithm.
 
     gamma : float, default=1.0
-        Scaling factor of RBF, polynomial, exponential chi^2 and
-        sigmoid affinity kernel. Ignored for
-        ``affinity='nearest_neighbors'``.
+        Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels.
+        Ignored for ``affinity='nearest_neighbors'``.
 
     degree : float, default=3
         Degree of the polynomial kernel. Ignored by other kernels.

sklearn/decomposition/kernel_pca.py

@@ -35,7 +35,7 @@ class KernelPCA(BaseEstimator, TransformerMixin):
         Degree for poly kernels. Ignored by other kernels.
 
     gamma : float, default=1/n_features
-        Kernel coefficient for rbf and poly kernels. Ignored by other
+        Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other
         kernels.
 
     coef0 : float, default=1

sklearn/kernel_approximation.py

@@ -382,8 +382,8 @@ class Nystroem(BaseEstimator, TransformerMixin):
         How many data points will be used to construct the mapping.
 
     gamma : float, default=None
-        Gamma parameter for the RBF, polynomial, exponential chi2 and
-        sigmoid kernels. Interpretation of the default value is left to
+        Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
+        and sigmoid kernels. Interpretation of the default value is left to
         the kernel; see the documentation for sklearn.metrics.pairwise.
         Ignored by other kernels.
 

sklearn/svm/libsvm.pyx

@@ -88,8 +88,8 @@ def fit(
         set to polynomial), 3 by default.
 
     gamma : float64, optional
-        Gamma parameter in RBF kernel (only relevant if kernel is set
-        to RBF). 0.1 by default.
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels. 0.1 by default.
 
     coef0 : float64, optional
         Independent parameter in poly/sigmoid kernel. 0 by default.
@@ -295,7 +295,8 @@ def predict(np.ndarray[np.float64_t, ndim=2, mode='c'] X,
     degree : int
         Degree of the polynomial kernel.
     gamma : float
-        Gamma parameter in RBF kernel.
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels. 0.1 by default.
     coef0 : float
         Independent parameter in poly/sigmoid kernel.
 
@@ -494,8 +495,8 @@ def cross_validation(
         set to polynomial)
 
     gamma : float
-        Gamma parameter in RBF kernel (only relevant if kernel is set
-        to RBF)
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels. 0.1 by default.
 
     coef0 : float
         Independent parameter in poly/sigmoid kernel.