Build a gradient boosting model from the training set (X, y). Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] Input feature matrix. y : array-like of shape = [n_samples] The target values (class labels
(self, X, y,
sample_weight=None, init_score=None, group=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_class_weight=None, eval_init_score=None, eval_group=None,
eval_metric=None, early_stopping_rounds=None, verbose=True,
feature_name='auto', categorical_feature='auto',
callbacks=None, init_model=None)
| 371 | return self |
| 372 | |
| 373 | def fit(self, X, y, |
| 374 | sample_weight=None, init_score=None, group=None, |
| 375 | eval_set=None, eval_names=None, eval_sample_weight=None, |
| 376 | eval_class_weight=None, eval_init_score=None, eval_group=None, |
| 377 | eval_metric=None, early_stopping_rounds=None, verbose=True, |
| 378 | feature_name='auto', categorical_feature='auto', |
| 379 | callbacks=None, init_model=None): |
| 380 | """Build a gradient boosting model from the training set (X, y). |
| 381 | |
| 382 | Parameters |
| 383 | ---------- |
| 384 | X : array-like or sparse matrix of shape = [n_samples, n_features] |
| 385 | Input feature matrix. |
| 386 | y : array-like of shape = [n_samples] |
| 387 | The target values (class labels in classification, real numbers in regression). |
| 388 | sample_weight : array-like of shape = [n_samples] or None, optional (default=None) |
| 389 | Weights of training data. |
| 390 | init_score : array-like of shape = [n_samples] or None, optional (default=None) |
| 391 | Init score of training data. |
| 392 | group : array-like or None, optional (default=None) |
| 393 | Group data of training data. |
| 394 | eval_set : list or None, optional (default=None) |
| 395 | A list of (X, y) tuple pairs to use as validation sets. |
| 396 | eval_names : list of strings or None, optional (default=None) |
| 397 | Names of eval_set. |
| 398 | eval_sample_weight : list of arrays or None, optional (default=None) |
| 399 | Weights of eval data. |
| 400 | eval_class_weight : list or None, optional (default=None) |
| 401 | Class weights of eval data. |
| 402 | eval_init_score : list of arrays or None, optional (default=None) |
| 403 | Init score of eval data. |
| 404 | eval_group : list of arrays or None, optional (default=None) |
| 405 | Group data of eval data. |
| 406 | eval_metric : string, list of strings, callable or None, optional (default=None) |
| 407 | If string, it should be a built-in evaluation metric to use. |
| 408 | If callable, it should be a custom evaluation metric, see note below for more details. |
| 409 | In either case, the ``metric`` from the model parameters will be evaluated and used as well. |
| 410 | Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker. |
| 411 | early_stopping_rounds : int or None, optional (default=None) |
| 412 | Activates early stopping. The model will train until the validation score stops improving. |
| 413 | Validation score needs to improve at least every ``early_stopping_rounds`` round(s) |
| 414 | to continue training. |
| 415 | Requires at least one validation data and one metric. |
| 416 | If there's more than one, will check all of them. But the training data is ignored anyway. |
| 417 | To check only the first metric, set the ``first_metric_only`` parameter to ``True`` |
| 418 | in additional parameters ``**kwargs`` of the model constructor. |
| 419 | verbose : bool or int, optional (default=True) |
| 420 | Requires at least one evaluation data. |
| 421 | If True, the eval metric on the eval set is printed at each boosting stage. |
| 422 | If int, the eval metric on the eval set is printed at every ``verbose`` boosting stage. |
| 423 | The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed. |
| 424 | |
| 425 | .. rubric:: Example |
| 426 | |
| 427 | With ``verbose`` = 4 and at least one item in ``eval_set``, |
| 428 | an evaluation metric is printed every 4 (instead of 1) boosting stages. |
| 429 | |
| 430 | feature_name : list of strings or 'auto', optional (default='auto') |