Method fit

sklearn/kernel_ridge.py:181–223

Fit Kernel Ridge regression model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. If kernel == "precomputed" this is instead a precomputed kernel matrix, of shape (n_samples, n_samples).

(self, X, y, sample_weight=None)

    @_fit_context(prefer_skip_nested_validation=True)
    def fit(self, X, y, sample_weight=None):
        """Fit Kernel Ridge regression model.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Training data. If kernel == "precomputed" this is instead
            a precomputed kernel matrix, of shape (n_samples, n_samples).

        y : array-like of shape (n_samples,) or (n_samples, n_targets)
            Target values.

        sample_weight : float or array-like of shape (n_samples,), default=None
            Individual weights for each sample, ignored if None is passed.

        Returns
        -------
        self : object
            Returns the instance itself.
        """
        # Convert data
        X, y = validate_data(
            self, X, y, accept_sparse=("csr", "csc"), multi_output=True, y_numeric=True
        )
        if sample_weight is not None and not isinstance(sample_weight, float):
            sample_weight = _check_sample_weight(sample_weight, X)

        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

    def predict(self, X):
        """Predict using the kernel ridge model.