Class Nystroem

sklearn/kernel_approximation.py:859–1137

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 `. .. versionadded:: 0.13 Parameters ---------- k

class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
    """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
    ----------