class_docstr: Kernel ridge regression.
  
  Kernel ridge regression (KRR) combines ridge regression (linear least
  squares with l2-norm regularization) with the kernel trick. It thus
  learns a linear function in the space induced by the respective kernel and
  the data. For non-linear kernels, this corresponds to a non-linear
  function in the original space.
  
  The form of the model learned by KRR is identical to support vector
  regression (SVR). However, different loss functions are used: KRR uses
  squared error loss while support vector regression uses epsilon-insensitive
  loss, both combined with l2 regularization. In contrast to SVR, fitting a
  KRR model can be done in closed-form and is typically faster for
  medium-sized datasets. On the other  hand, the learned model is non-sparse
  and thus slower than SVR, which learns a sparse model for epsilon > 0, at
  prediction-time.
  
  This estimator has built-in support for multi-variate regression
  (i.e., when y is a 2d-array of shape [n_samples, n_targets]).
  
  Read more in the :ref:`User Guide <kernel_ridge>`.
  
  Parameters
  ----------
  alpha : {float, array-like}, shape = [n_targets]
      Small positive values of alpha improve the conditioning of the problem
      and reduce the variance of the estimates.  Alpha corresponds to
      ``(2*C)^-1`` in other linear models such as LogisticRegression or
      LinearSVC. If an array is passed, penalties are assumed to be specific
      to the targets. Hence they must correspond in number.
  
  kernel : string or callable, default="linear"
      Kernel mapping used internally. 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.
  
  degree : float, default=3
      Degree of the polynomial kernel. Ignored by other kernels.
  
  coef0 : float, default=1
      Zero coefficient for polynomial and sigmoid kernels.
      Ignored by other kernels.
  
  kernel_params : mapping of string to any, optional
      Additional parameters (keyword arguments) for kernel function passed
      as callable object.
  
  Attributes
  ----------
  dual_coef_ : array, shape = [n_samples] or [n_samples, n_targets]
      Representation of weight vector(s) in kernel space
  
  X_fit_ : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training data, which is also required for prediction
  
  References
  ----------
  * Kevin P. Murphy
    "Machine Learning: A Probabilistic Perspective", The MIT Press
    chapter 14.4.3, pp. 492-493
  
  See also
  --------
  Ridge
      Linear ridge regression.
  SVR
      Support Vector Regression implemented using libsvm.
  
  Examples
  --------
  >>> from sklearn.kernel_ridge import KernelRidge
  >>> 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)
  >>> clf = KernelRidge(alpha=1.0)
  >>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
  KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear',
              kernel_params=None)
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docstr: Fit Kernel Ridge regression model
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training data
  
  y : array-like, shape = [n_samples] or [n_samples, n_targets]
      Target values
  
  sample_weight : float or numpy array of shape [n_samples]
      Individual weights for each sample, ignored if None is passed.
  
  Returns
  -------
  self : returns an instance of self.
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code: def fit(self, X, y=None, sample_weight=None):
      """Fit Kernel Ridge regression model
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Training data
  
      y : array-like, shape = [n_samples] or [n_samples, n_targets]
          Target values
  
      sample_weight : float or numpy array of shape [n_samples]
          Individual weights for each sample, ignored if None is passed.
  
      Returns
      -------
      self : returns an instance of self.
      """
      # Convert data
      X, y = check_X_y(
          X,
          y,
          accept_sparse=("csr", "csc"),
          multi_output=True,
          y_numeric=True,
      )
  
      K = self._get_kernel(X)
      alpha = np.atleast_1d(self.alpha)
  
      ravel = False
      if len(y.shape) == 1:
          y = y.reshape(-1, 1)
          ravel = True
  
      copy = self.kernel == "precomputed"
      self.dual_coef_ = _solve_cholesky_kernel(
          K, y, alpha, sample_weight, copy
      )
      if ravel:
          self.dual_coef_ = self.dual_coef_.ravel()
  
      self.X_fit_ = X
  
      return self
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/kernel_ridge.py
imports: import numpy
  from .base import BaseEstimator, RegressorMixin
  from .metrics.pairwise import pairwise_kernels
  from .linear_model.ridge import _solve_cholesky_kernel
  from .utils import check_X_y
  from .utils.validation import check_is_fitted
label_desc: Use image node_id 4 for calling the KernelRidge obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self