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Fitting tree models with monotonicity constraints can be useful in a variety of scenarios and can also help with model interpretability. The XGBoost documentation provides an excellent overview of why this can be useful. For some use cases, monotonicity constraints can be in the critical path when choosing an algorithm or framework.
Fitting tree models with monotonicity constraints can be useful in a variety of scenarios and can also help with model interpretability. The XGBoost documentation provides an excellent overview of why this can be useful. For some use cases, monotonicity constraints can be in the critical path when choosing an algorithm or framework.
There's an open scikit-learn issue requesting the feature and there's an active PR that is implementing this functionality.
It would be great to support this in cuML with an API consistent with what the scikit-learn community is standardizing on.
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