evals
list and for every metric specified. For example with evals = [(dtrain, "train"), (dval, "valid")]
and "eval_metric": ["mae", "rmse"]
, 4 metrics are created - "train/mae"
, "train/rmse"
, "valid/mae"
, and "valid/rmse"
. xgboost.train()
and xgboost.cv()
functions, and with the sklearn API model.fit()
. xgboost>=1.3.0
. str
, optional, default is "training"
) - Namespace under which all metadata logged by the NeptuneCallback will be stored.bool
, default is True
) - Whether to log model as a pickled file at the end of training.bool
, default is True
) - Whether to log feature importance charts at the end of training.int
, default is None
) - Max number of top features on the importance charts. Works only if log_importance
is set to True
. If None
, all features will be displayed. See xgboost.plot_importance
for details.list
of int
, default is None
) - Indices of the target trees to log as charts. See xgboost.to_graphviz
for details.int
, default is 30) - Control size of the visualized tree image. Increase this in case you work with large trees. Works only if log_tree
is not None
.