Fits every learner to a subset sampled with replacement from X. Calling this method makes the learner forget the data it has seen up until this point and replaces it with X! If you would like to perform bootstrapping on each learner using the data it has seen, use the method
(self, X: modALinput, y: modALinput, **fit_kwargs)
| 511 | super()._add_training_data(X, y) |
| 512 | |
| 513 | def fit(self, X: modALinput, y: modALinput, **fit_kwargs) -> 'BaseCommittee': |
| 514 | """ |
| 515 | Fits every learner to a subset sampled with replacement from X. Calling this method makes the learner forget the |
| 516 | data it has seen up until this point and replaces it with X! If you would like to perform bootstrapping on each |
| 517 | learner using the data it has seen, use the method .rebag()! |
| 518 | Calling this method makes the learner forget the data it has seen up until this point and replaces it with X! |
| 519 | Args: |
| 520 | X: The samples to be fitted on. |
| 521 | y: The corresponding labels. |
| 522 | **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor. |
| 523 | """ |
| 524 | super().fit(X, y, **fit_kwargs) |
| 525 | self._set_classes() |
| 526 | |
| 527 | def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs) -> None: |
| 528 | """ |
no test coverage detected