From eda1cf302eb44a81966bec028d2a0aeeeddf592a Mon Sep 17 00:00:00 2001 From: Archana Subramaniyan Date: Mon, 8 Jun 2020 14:15:46 -0700 Subject: [PATCH] Update default values in kernel_approximation doc string Updated default values of parameter in kernel_approximation doc string --- sklearn/kernel_approximation.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py index eda042bfed34e..d13c172a5d644 100644 --- a/sklearn/kernel_approximation.py +++ b/sklearn/kernel_approximation.py @@ -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.