|
6 | 6 | class FreqaiMultiOutputRegressor(MultiOutputRegressor): |
7 | 7 | def fit(self, X, y, sample_weight=None, fit_params=None): |
8 | 8 | """Fit the model to data, separately for each output variable. |
9 | | - Parameters |
10 | | - ---------- |
11 | | - X : {array-like, sparse matrix} of shape (n_samples, n_features) |
| 9 | + :param X: {array-like, sparse matrix} of shape (n_samples, n_features) |
12 | 10 | The input data. |
13 | | - y : {array-like, sparse matrix} of shape (n_samples, n_outputs) |
| 11 | + :param y: {array-like, sparse matrix} of shape (n_samples, n_outputs) |
14 | 12 | Multi-output targets. An indicator matrix turns on multilabel |
15 | 13 | estimation. |
16 | | - sample_weight : array-like of shape (n_samples,), default=None |
| 14 | + :param sample_weight: array-like of shape (n_samples,), default=None |
17 | 15 | Sample weights. If `None`, then samples are equally weighted. |
18 | 16 | Only supported if the underlying regressor supports sample |
19 | 17 | weights. |
20 | | - fit_params : A list of dicts for the fit_params |
| 18 | +
|
| 19 | + :param fit_params: A list of dicts for the fit_params |
21 | 20 | Parameters passed to the ``estimator.fit`` method of each step. |
22 | 21 | Each dict may contain same or different values (e.g. different |
23 | 22 | eval_sets or init_models) |
24 | | - .. versionadded:: 0.23 |
25 | | - Returns |
26 | | - ------- |
27 | | - self : object |
28 | | - Returns a fitted instance. |
| 23 | +
|
29 | 24 | """ |
30 | 25 |
|
31 | 26 | if not hasattr(self.estimator, "fit"): |
|
0 commit comments