arm_start>`.

copy_X_train : bool, default=True
    If True, a persistent copy of the training data is stored in the
    object. Otherwise, just a reference to the training data is stored,
    which might cause predictions to change if the data is modified
    externally.

random_state : int, RandomState instance or None, default=None
    Determines random number generation used to initialize the centers.
    Pass an int for reproducible results across multiple function calls.
    See :term:`Glossary <random_state>`.

Attributes
----------
X_train_ : array-like of shape (n_samples, n_features) or list of object
    Feature vectors or other representations of training data (also
    required for prediction).

y_train_ : array-like of shape (n_samples,)
    Target values in training data (also required for prediction)

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

kernel_ : kernl instance
    The kernel used for prediction. The structure of the kernel is the
    same as the one passed as parameter but with optimized hyperparameters

L_ : array-like of shape (n_samples, n_samples)
    Lower-triangular Cholesky decomposition of the kernel in X_train_

pi_ : array-like of shape (n_samples,)
    The probabilities of the positive class for the training points
    X_train_

W_sr_ : array-like of shape (n_samples,)
    Square root of W, the Hessian of log-likelihood of the latent function
    values for the observed labels. Since W is diagonal, only the diagonal
    of sqrt(W) is stored.

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

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>`_
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