Make a prediction. Parameters ---------- data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse Data source for prediction. If string, it represents the path to txt file. num_iteration : int or None, optional (defa
(self, data, num_iteration=None,
raw_score=False, pred_leaf=False, pred_contrib=False,
data_has_header=False, is_reshape=True, **kwargs)
| 2435 | return ret |
| 2436 | |
| 2437 | def predict(self, data, num_iteration=None, |
| 2438 | raw_score=False, pred_leaf=False, pred_contrib=False, |
| 2439 | data_has_header=False, is_reshape=True, **kwargs): |
| 2440 | """Make a prediction. |
| 2441 | |
| 2442 | Parameters |
| 2443 | ---------- |
| 2444 | data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse |
| 2445 | Data source for prediction. |
| 2446 | If string, it represents the path to txt file. |
| 2447 | num_iteration : int or None, optional (default=None) |
| 2448 | Limit number of iterations in the prediction. |
| 2449 | If None, if the best iteration exists, it is used; otherwise, all iterations are used. |
| 2450 | If <= 0, all iterations are used (no limits). |
| 2451 | raw_score : bool, optional (default=False) |
| 2452 | Whether to predict raw scores. |
| 2453 | pred_leaf : bool, optional (default=False) |
| 2454 | Whether to predict leaf index. |
| 2455 | pred_contrib : bool, optional (default=False) |
| 2456 | Whether to predict feature contributions. |
| 2457 | |
| 2458 | .. note:: |
| 2459 | |
| 2460 | If you want to get more explanations for your model's predictions using SHAP values, |
| 2461 | like SHAP interaction values, |
| 2462 | you can install the shap package (https://github.com/slundberg/shap). |
| 2463 | Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra |
| 2464 | column, where the last column is the expected value. |
| 2465 | |
| 2466 | data_has_header : bool, optional (default=False) |
| 2467 | Whether the data has header. |
| 2468 | Used only if data is string. |
| 2469 | is_reshape : bool, optional (default=True) |
| 2470 | If True, result is reshaped to [nrow, ncol]. |
| 2471 | **kwargs |
| 2472 | Other parameters for the prediction. |
| 2473 | |
| 2474 | Returns |
| 2475 | ------- |
| 2476 | result : numpy array |
| 2477 | Prediction result. |
| 2478 | """ |
| 2479 | predictor = self._to_predictor(copy.deepcopy(kwargs)) |
| 2480 | if num_iteration is None: |
| 2481 | num_iteration = self.best_iteration |
| 2482 | return predictor.predict(data, num_iteration, |
| 2483 | raw_score, pred_leaf, pred_contrib, |
| 2484 | data_has_header, is_reshape) |
| 2485 | |
| 2486 | def refit(self, data, label, decay_rate=0.9, **kwargs): |
| 2487 | """Refit the existing Booster by new data. |