t_svm_scale_c.py`.

kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,          default='rbf'
     Specifies the kernel type to be used in the algorithm.
     If none is given, 'rbf' will be used. If a callable is given it is
     used to precompute the kernel matrix.
     For an intuitive visualization of different kernel types see
     See :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`

degree : int, default=3
    Degree of the polynomial kernel function ('poly').
    Must be non-negative. Ignored by all other kernels.

gamma : {'scale', 'auto'} or float, default='scale'
    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.

    - if ``gamma='scale'`` (default) is passed then it uses
      1 / (n_features * X.var()) as value of gamma,
    - if 'auto', uses 1 / n_features
    - if float, must be non-negative.

    .. versionchanged:: 0.22
       The default value of ``gamma`` changed from 'auto' to 'scale'.

coef0 : float, default=0.0
    Independent term in kernel function.
    It is only significant in 'poly' and 'sigmoid'.

shrinking : bool, default=True
    Whether to use the shrinking heuristic.
    See the :ref:`User Guide <shrinking_svm>`.

tol : float, default=1e-3
    Tolerance for stopping criterion.

cache_size : float, default=200
    Specify the size of the kernel cache (in MB).

verbose : bool, default=False
    Enable verbose output. Note that this setting takes advantage of a
    per-process runtime setting in libsvm that, if enabled, may not work
    properly in a multithreaded context.

max_iter : int, default=-1
    Hard limit on iterations within solver, or -1 for no limit.

Attributes
----------
coef_ : ndarray of shape (1, n_features)
    Weights assigned to the features (coefficients in the primal
    problem). This is only available in the case of a linear kernel.

    `coef_` is readonly property derived from `dual_coef_` and
    `support_vectors_`.

dual_coef_ : ndarray of shape (1, n_SV)
    Coefficients of the support vector in the decision function.

fit_status_ : int
    0 if correctly fitted, 1 otherwise (will raise warning)

intercept_ : ndarray of shape (1,)
    Constants in decision function.

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

n_iter_ : int
    Number of iterations run by the optimization routine to fit the model.

    .. versionadded:: 1.1

n_support_ : ndarray of shape (1,), dtype=int32
    Number of support vectors.

shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
    Array dimensions of training vector ``X``.

support_ : ndarray of shape (n_SV,)
    Indices of support vectors.

support_vectors_ : ndarray of shape (n_SV, n_features)
    Support vectors.

See Also
--------
NuSVC : Support Vector Machine for classification implemented with libsvm
    with a parameter to control the number of support vectors.

SVR : Epsilon Support Vector Machine for regression implemented with
    libsvm.

References
----------
.. [1] `LIBSVM: A Library for Support Vector Machines
    <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_

.. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
    Machines and Comparisons to Regularized Likelihood Methods"
    <https://citeseerx.ist.psu.edu/doc_view/pid/42e5ed832d4310ce4378c44d05570439df28a393>`_

Examples
--------
>>> from sklearn.svm import NuSVR
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> regr = make_pipeline(StandardScaler(), NuSVR(C=1.0, nu=0.1))
>>> regr.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('nusvr', NuSVR(nu=0.1))])
