8000 DOC Update default values in kernel_approximation doc string by asubramaniyan · Pull Request #17536 · scikit-learn/scikit-learn · GitHub
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DOC Update default values in kernel_approximation doc string #17536

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Jun 8, 2020
+6 −6
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DOC Update default values in kernel_approximation doc string #17536

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12 changes: 6 additions & 6 deletions sklearn/kernel_approximation.py
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Expand Up @@ -32,10 +32,10 @@ class RBFSampler(TransformerMixin, BaseEstimator):

Para 8000 meters
----------
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.

Expand Down Expand Up @@ -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.

Expand Down Expand Up @@ -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.
Expand All @@ -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.

Expand Down
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