num_rows
) and number of features (num_features
). Evaluation results are logged separately for every valid_sets
. For example with "metric": "logloss"
and valid_names=["train","valid"]
, 2 logs are created: train/logloss
and valid/logloss
. lgbm.train()
and lgbm.cv()
functions, and with the scikit-learn API model.fit()
.str
, optional, default is None
) - Namespace under which all metadata logged by the NeptuneCallback will be stored.bool
, default is True
) - Whether to log feature importance charts.int
, default is 10
) - Max number of top features to log on the importance charts. Works only if log_importances
is set to True
. If None
or <1
, all features will be displayed. See lightgbm.plot_importance
for details.list
of int
, default is None
) - Indices of the target tree to visualize. Works only if log_trees
is set to True
.bool
, default is True
) - Whether to parse the model and log trees in the easy-to-read pandas DataFrame format. Works only for Booster
objects. See lightgbm.Booster.trees_to_dataframe
for details.bool
, default is True
) - Whether to log the model as a pickled file.bool
, default is False
) - Whether to log visualized trees. This requires graphviz library to work, read how to install it in the user guide. int
, default is 30
) - Control size of the visualized tree image. Increase this in case you work with large trees. Works only if log_trees
is set to True
.bool
, default is False
) - Whether to log confusion matrix. If set to True, you need to pass y_true
and y_pred
.numpy.array
, default is None
) - True labels on the test set. Needed only if log_confusion_matrix
is set to True
.numpy.array
, default is None
) - Predictions on the test set. Needed only if log_confusion_matrix
is set to True
.dict
with all metadata, that can be assigned to the run namespace. run["booster_summary"] = create_booster_summary(...)