DOC Update default values in kernel_approximation doc string
Updated default values of parameter in kernel_approximation doc string
@@ -32,10 +32,10 @@ class RBFSampler(TransformerMixin, BaseEstimator):
Parameters
----------
- gamma : float
+ gamma : float, default=1.0
Parameter of RBF kernel: exp(-gamma * x^2)
- n_components : int
+ n_components : int, default=100
Number of Monte Carlo samples per original feature.
Equals the dimensionality of the computed feature space.
@@ -146,10 +146,10 @@ class SkewedChi2Sampler(TransformerMixin, BaseEstimator):
Parameters
----------
- skewedness : float
+ skewedness : float, default=1.0
"skewedness" parameter of the kernel. Needs to be cross-validated.
- n_components : int
+ n_components : int, default=100
number of Monte Carlo samples per original feature.
Equals the dimensionality of the computed feature space.
@@ -455,7 +455,7 @@ class Nystroem(TransformerMixin, BaseEstimator):
Parameters
----------
- kernel : string or callable, default="rbf"
+ kernel : string or callable, default='rbf'
Kernel map to be approximated. A callable should accept two arguments
and the keyword arguments passed to this object as kernel_params, and
should return a floating point number.
@@ -477,7 +477,7 @@ class Nystroem(TransformerMixin, BaseEstimator):
Additional parameters (keyword arguments) for kernel function passed
as callable object.
- n_components : int
+ n_components : int, default=100
Number of features to construct.
How many data points will be used to construct the mapping.
