class_docstr: A wrapper class for KernelPCA class of scikit-learn.
class_docstr_tok: ["A","wrapper","class","for","KernelPCA","class","of","scikit-learn","."]
code: def __init__(
      self,
      n_components=None,
      kernel="rbf",
      gamma=None,
      degree=3,
      coef0=1,
      kernel_params=None,
      alpha=1.0,
      fit_inverse_transform=False,
      eigen_solver="auto",
      tol=0,
      max_iter=None,
      remove_zero_eig=False,
      copy_X=True,
      n_jobs=None,
      random_state=None,
  ):
      super().__init__(
          kernel=kernel,
          gamma=gamma,
          degree=degree,
          coef0=coef0,
          kernel_params=kernel_params,
          alpha=alpha,
          fit_inverse_transform=fit_inverse_transform,
          eigen_solver=eigen_solver,
          tol=tol,
          max_iter=max_iter,
          remove_zero_eig=remove_zero_eig,
          n_jobs=n_jobs,
          copy_X=copy_X,
          random_state=check_random_state(random_state),
      )
  
file_path: pyod/pyod/models/kpca.py
imports: import numpy
  from sklearn.decomposition import KernelPCA
  from sklearn.utils import check_array, check_random_state
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
  from ..utils.utility import check_parameter
label_desc: Use image node_id 1 to create a new PyODKernelPCA object from inherited base classes: KernelPCA with example: obj = PyODKernelPCA(n_components, kernel, gamma, degree, coef0, kernel_params, alpha, fit_inverse_transform, eigen_solver, tol, max_iter, remove_zero_eig, copy_X, n_jobs, random_state)