QSVR.fit

QSVR.fit(X, y, sample_weight=None)

Fit the SVM model according to the given training data.

Parameters:

• X ({array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)) – Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).

• y (array-like of shape (n_samples,)) – Target values (class labels in classification, real numbers in regression).

• sample_weight (array-like of shape (n_samples,), default=None) – Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns:

self – Fitted estimator.

Return type:

object

Notes

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.