darray 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
--------
NuSVR : Support Vector Machine for regression implemented using libsvm
    using a parameter to control the number of support vectors.

LinearSVR : Scalable Linear Support Vector Machine for regression
    implemented using liblinear.

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 SVR
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))
>>> regr.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('svr', SVR(epsilon=0.2))])
