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related to Feature importance for random forest classification of a sampleFeature importance for random forest classification of a sample

This blog by Ando Saabas suggests a nice way to interpret a tree result for a specific sample into per-feature contributions. Basically he goes over the path to the sample and counts each delta in the prediction $pred(currentNode) - pred(parentNode)$ as a contribution for the splitting feature.

Can I use a similar approach for interpreting results of gradient boosting with tree weak classifiers, specifically for the popular xgboost implementation?

related to Feature importance for random forest classification of a sample

This blog by Ando Saabas suggests a nice way to interpret a tree result for a specific sample into per-feature contributions. Basically he goes over the path to the sample and counts each delta in the prediction $pred(currentNode) - pred(parentNode)$ as a contribution for the splitting feature.

Can I use a similar approach for interpreting results of gradient boosting with tree weak classifiers, specifically for the popular xgboost implementation?

related to Feature importance for random forest classification of a sample

This blog by Ando Saabas suggests a nice way to interpret a tree result for a specific sample into per-feature contributions. Basically he goes over the path to the sample and counts each delta in the prediction $pred(currentNode) - pred(parentNode)$ as a contribution for the splitting feature.

Can I use a similar approach for interpreting results of gradient boosting with tree weak classifiers, specifically for the popular xgboost implementation?

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ihadanny
ihadanny
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Feature importance for xgboost classification of a sample

related to Feature importance for random forest classification of a sample

This blog by Ando Saabas suggests a nice way to interpret a tree result for a specific sample into per-feature contributions. Basically he goes over the path to the sample and counts each delta in the prediction $pred(currentNode) - pred(parentNode)$ as a contribution for the splitting feature.

Can I use a similar approach for interpreting results of gradient boosting with tree weak classifiers, specifically for the popular xgboost implementation?