 'one_vs_one'. In 'one_vs_rest',
    one binary Gaussian process classifier is fitted for each class, which
    is trained to separate this class from the rest. In 'one_vs_one', one
    binary Gaussian process classifier is fitted for each pair of classes,
    which is trained to separate these two classes. The predictions of
    these binary predictors are combined into multi-class predictions.
    Note that 'one_vs_one' does not support predicting probability
    estimates.

n_jobs : int, default=None
    The number of jobs to use for the computation: the specified
    multiclass problems are computed 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.

Attributes
----------
base_estimator_ : ``Estimator`` instance
    The estimator instance that defines the likelihood function
    using the observed data.

kernel_ : kernel instance
    The kernel used for prediction. In case of binary classification,
    the structure of the kernel is the same as the one passed as parameter
    but with optimized hyperparameters. In case of multi-class
    classification, a CompoundKernel is returned which consists of the
    different kernels used in the one-versus-rest classifiers.

log_marginal_likelihood_value_ : float
    The log-marginal-likelihood of ``self.kernel_.theta``

classes_ : array-like of shape (n_classes,)
    Unique class labels.

n_classes_ : int
    The number of classes in the training data

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
--------
GaussianProcessRegressor : Gaussian process regression (GPR).

References
----------
.. [RW2006] `Carl E. Rasmussen and Christopher K.I. Williams,
   "Gaussian Processes for Machine Learning",
   MIT Press 2006 <https://www.gaussianprocess.org/gpml/chapters/RW.pdf>`_

Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn.gaussian_process import GaussianProcessClassifier
>>> from sklearn.gaussian_process.kernels import RBF
>>> X, y = load_iris(return_X_y=True)
>>> kernel = 1.0 * RBF(1.0)
>>> gpc = GaussianProcessClassifier(kernel=kernel,
...         random_state=0).fit(X, y)
>>> gpc.score(X, y)
0.9866...
>>> gpc.predict_proba(X[:2,:])
array([[0.83548752, 0.03228706, 0.13222543],
       [0.79064206, 0.06525643, 0.14410151]])

For a comaprison of the GaussianProcessClassifier with other classifiers see:
:ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`.
Nr 