learned_weights_combiner#
Learned Weights Combiner.
Forecast combiner that uses a classification approach to learn weights for base forecasters. It learns which forecaster is likely to perform best under different conditions.
The combiner can operate in two modes:
Hard Selection: Selects the base forecaster with the highest predicted probability for each instance.
Soft Selection: Uses the predicted probabilities as weights to combine base forecaster predictions.
Classes#
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Hyperparameters for the LGBM gradient-boosted classifier. |
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Hyperparameters for the logistic regression classifier. |
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Hyperparameters for the LGBM random-forest classifier. |
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Combines base forecaster predictions with a classification approach. |
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Hyperparameters for the XGBoost classifier. |