class_docstr: Approximate a kernel map using a subset of the training data.
  
  Constructs an approximate feature map for an arbitrary kernel
  using a subset of the data as basis.
  
  Read more in the :ref:`User Guide <nystroem_kernel_approx>`.
  
  .. versionadded:: 0.13
  
  Parameters
  ----------
  kernel : str 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.
  
  gamma : float, default=None
      Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
      and sigmoid kernels. Interpretation of the default value is left to
      the kernel; see the documentation for sklearn.metrics.pairwise.
      Ignored by other kernels.
  
  coef0 : float, default=None
      Zero coefficient for polynomial and sigmoid kernels.
      Ignored by other kernels.
  
  degree : float, default=None
      Degree of the polynomial kernel. Ignored by other kernels.
  
  kernel_params : dict, default=None
      Additional parameters (keyword arguments) for kernel function passed
      as callable object.
  
  n_components : int, default=100
      Number of features to construct.
      How many data points will be used to construct the mapping.
  
  random_state : int, RandomState instance or None, default=None
      Pseudo-random number generator to control the uniform sampling without
      replacement of `n_components` of the training data to construct the
      basis kernel.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  n_jobs : int, default=None
      The number of jobs to use for the computation. This works by breaking
      down the kernel matrix into `n_jobs` even slices and computing them in
      parallel.
  
      ``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:: 0.24
  
  Attributes
  ----------
  components_ : ndarray of shape (n_components, n_features)
      Subset of training points used to construct the feature map.
  
  component_indices_ : ndarray of shape (n_components)
      Indices of ``components_`` in the training set.
  
  normalization_ : ndarray of shape (n_components, n_components)
      Normalization matrix needed for embedding.
      Square root of the kernel matrix on ``components_``.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel.
  PolynomialCountSketch : Polynomial kernel approximation via Tensor Sketch.
  RBFSampler : Approximate a RBF kernel feature map using random Fourier
      features.
  SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel.
  sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
  
  References
  ----------
  * Williams, C.K.I. and Seeger, M.
    "Using the Nystroem method to speed up kernel machines",
    Advances in neural information processing systems 2001
  
  * T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou
    "Nystroem Method vs Random Fourier Features: A Theoretical and Empirical
    Comparison",
    Advances in Neural Information Processing Systems 2012
  
  Examples
  --------
  >>> from sklearn import datasets, svm
  >>> from sklearn.kernel_approximation import Nystroem
  >>> X, y = datasets.load_digits(n_class=9, return_X_y=True)
  >>> data = X / 16.
  >>> clf = svm.LinearSVC(dual="auto")
  >>> feature_map_nystroem = Nystroem(gamma=.2,
  ...                                 random_state=1,
  ...                                 n_components=300)
  >>> data_transformed = feature_map_nystroem.fit_transform(data)
  >>> clf.fit(data_transformed, y)
  LinearSVC(dual='auto')
  >>> clf.score(data_transformed, y)
  0.9987...
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code: def __init__(
      self,
      kernel="rbf",
      *,
      gamma=None,
      coef0=None,
      degree=None,
      kernel_params=None,
      n_components=100,
      random_state=None,
      n_jobs=None,
  ):
      self.kernel = kernel
      self.gamma = gamma
      self.coef0 = coef0
      self.degree = degree
      self.kernel_params = kernel_params
      self.n_components = n_components
      self.random_state = random_state
      self.n_jobs = n_jobs
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from scipy.linalg import svd
  from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from .metrics.pairwise import KERNEL_PARAMS, PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels
  from .utils import check_random_state, deprecated
  from .utils._param_validation import Interval, StrOptions
  from .utils.extmath import safe_sparse_dot
  from .utils.validation import _check_feature_names_in, check_is_fitted, check_non_negative
label_desc: Use image node_id 1 to create a new Nystroem object from inherited base classes: ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator with example: obj = Nystroem(kernel)