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trees according to each estimator:
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- `n_estimators` controls the number of trees in the forest. It's a fixed number.
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- - `max_iter` is the the maximum number of iterations in a gradient boosting
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+ - `max_iter` is the maximum number of iterations in a gradient boosting
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based model. The number of iterations corresponds to the number of trees for
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regression and binary classification problems. Furthermore, the actual number
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of trees required by the model depends on the stopping criteria.
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# models uniformly dominate the Random Forest models in the "test score vs
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# training speed trade-off" (the HGBDT curve should be on the top left of the RF
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# curve, without ever crossing). The "test score vs prediction speed" trade-off
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- # can also be more disputed but it's most often favorable to HGBDT. It's always
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+ # can also be more disputed, but it's most often favorable to HGBDT. It's always
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# a good idea to check both kinds of model (with hyper-parameter tuning) and
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# compare their performance on your specific problem to determine which model is
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# the best fit but **HGBT almost always offers a more favorable speed-accuracy
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