Add number_samples to XGBoost ML Models
#398
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Since this is a community submitted pull request, a Jenkins build has not been kicked off automatically. Can an Elastic organization member please verify the contents of this patch and then kick off a build manually? |
benwtrent
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Sep 30, 2021
| @@ -103,6 +107,7 @@ def build_tree_node(self, row: pd.Series, curr_tree: int) -> TreeNode: | |||
| right_child=self.extract_node_id(row["No"], curr_tree), | |||
| threshold=float(row["Split"]), | |||
| split_feature=self.get_feature_id(row["Feature"]), | |||
| number_samples=int(row["Cover"]), | |||
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I don't think this is gonna work.
cover is a float. I don't know how they calculate cover related to the total number of samples that hit the tree. The docs indicate its the average of the total number of samples effected by the split.
I wonder if cover * number of features ~= The total number of docs? Can you investigate and confirm?
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Work on #243
TreeNode of
xgboosthas the following information:So, based on
importance_typeparameter of https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressorI am thinking
Covergives us the number of training samples that is being covered by the node or leaf since "Gain" is already used asleaf_value. I might be wrong though!@benwtrent Please give a review :)