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Update python_scripts/ensemble_hyperparameters.py
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Co-authored-by: Arturo Amor <[email protected]>
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fritshermans and ArturoAmorQ authored Jan 29, 2025
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# it can be beneficial to increase `max_iter` if `max_depth` is low.
#
# Finally, we have overlooked the impact of the `learning_rate` parameter
# until now. This parameter controls how much each tree contributes to the
# until now. This parameter controls how much each correction contributes to the
# final prediction. A smaller learning-rate means the corrections of a new
# tree result in small adjustments to the model prediction. When the
# learning-rate is small, the model generally needs more trees to achieve good
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