XGBoost

DVCLive allows you to add experiment tracking capabilities to your XGBoost projects.

Usage

Include the DVCLiveCallback in the callbacks list passed to the xgboost.train call:

from dvclive.xgb import DVCLiveCallback

...

model = xgb.XGBClassifier(
    n_estimators=100,
    early_stopping_rounds=5,
    eval_metric=["merror", "mlogloss"],
    callbacks=[DVCLiveCallback()]
)

model.fit(
    X_train,
    y_train,
    eval_set=[(X_test, y_test)]
)

Parameters

live - (None by default) - Optional Live instance. If None, a new instance will be created using **kwargs.
**kwargs - Any additional arguments will be used to instantiate a new Live instance. If live is used, the arguments are ignored.

Examples

Using live to pass an existing Live instance.

from dvclive import Live
from dvclive.xgb import DVCLiveCallback

...

with Live("custom_dir") as live:
    model = xgb.XGBClassifier(
        n_estimators=100,
        early_stopping_rounds=5,
        eval_metric=["merror", "mlogloss"],
        callbacks=[DVCLiveCallback()]
    )

    model.fit(
        X_train,
        y_train,
        eval_set=[(X_test, y_test)]
    )

    # Log additional metrics after training
    live.log_metric("summary_metric", 1.0, plot=False)

Using **kwargs to customize Live.

model = xgb.XGBClassifier(
    ...
    callbacks=[DVCLiveCallback(dir="custom_dir")]
)

XGBoost

Usage

Parameters

Examples

Content