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Models

EraBoostedXGBRegressor

NOTE: This is still an experimental feature and subject to change.

EraBoostedXGBRegressor is a custom regressor extending the functionality of XGBoost, aimed at improving accuracy on specific eras in a dataset. It upweights the eras that are toughest to fit. It is designed to integrate seamlessly with scikit-learn.

Why?

  • Era-Specific Focus: Targets the worst-performing eras in your data for performance enhancement, ensuring that the model improves where it is most needed.
  • Scikit-learn integration: EraBoostedXGBRegressor is designed to integrate seamlessly with scikit-learn.
  • Customization Options: Offers flexibility to adjust the proportion of eras to focus on, the number of trees added per iteration, and the total number of iterations for era boosting.

Quickstart

Make sure to include the era column as a pd.Series in the fit method.

from numerblox.models import EraBoostedXGBRegressor

model = EraBoostedXGBRegressor(proportion=0.5, trees_per_step=10, num_iters=20)
model.fit(X=X_train, y=y_train, era_series=eras_train)

predictions = model.predict(X_live)