17740
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sklearn.decomposition._kernel_pca.KernelPCA
sklearn.KernelPCA
sklearn.decomposition._kernel_pca.KernelPCA
1
openml==0.10.2,sklearn==0.22.1
Kernel Principal component analysis (KPCA)
Non-linear dimensionality reduction through the use of kernels (see
:ref:`metrics`).
2020-05-18T23:48:33
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
alpha
int
4
Hyperparameter of the ridge regression that learns the
inverse transform (when fit_inverse_transform=True)
coef0
float
1
Independent term in poly and sigmoid kernels
Ignored by other kernels
copy_X
boolean
false
If True, input X is copied and stored by the model in the `X_fit_`
attribute. If no further changes will be done to X, setting
`copy_X=False` saves memory by storing a reference
.. versionadded:: 0.18
degree
int
3
Degree for poly kernels. Ignored by other kernels
eigen_solver
string
"arpack"
Select eigensolver to use. If n_components is much less than
the number of training samples, arpack may be more efficient
than the dense eigensolver
fit_inverse_transform
bool
true
Learn the inverse transform for non-precomputed kernels
(i.e. learn to find the pre-image of a point)
gamma
float
null
Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other
kernels
kernel
"cosine"
kernel_params
mapping of string to any
null
Parameters (keyword arguments) and values for kernel passed as
callable object. Ignored by other kernels
max_iter
int
524
Maximum number of iterations for arpack
If None, optimal value will be chosen by arpack
n_components
int
4
Number of components. If None, all non-zero components are kept
kernel : "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
Kernel. Default="linear"
n_jobs
int or None
1
The number of parallel jobs to run
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
.. versionadded:: 0.18
random_state
int
42
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`. Used when ``eigen_solver`` == 'arpack'
.. versionadded:: 0.18
remove_zero_eig
boolean
false
If True, then all components with zero eigenvalues are removed, so
that the number of components in the output may be < n_components
(and sometimes even zero due to numerical instability)
When n_components is None, this parameter is ignored and components
with zero eigenvalues are removed regardless
tol
float
0.6476998368285369
Convergence tolerance for arpack
If 0, optimal value will be chosen by arpack
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1