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1 vote

Logloss worse than random guessing with xgboost

Log loss is a strictly proper scoring rule, which means that it can be decomposed into measures of calibration (if the predicted probabilities align with the reality of event occurrence frequency) and ...
Dave's user avatar
  • 64.9k
2 votes
Accepted

Efficient prediction using Lightgbm/XGBoost when varying single feature keeping the remaining constant

The problem is that in principle, boosting can model any kind of interaction between your fixed $C$ and your varying $z$, so even if $C$ is fixed, its particular setting may have any impact on the ...
Stephan Kolassa's user avatar
2 votes

(THEORY) Do Tree models output probabilities?

I think this can be made simpler. Single independent variable, and classification. In this case, I would argue, tree-based model is not that dissimilar from learning the distribution of your binary ...
Cryo's user avatar
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9 votes

(THEORY) Do Tree models output probabilities?

Your assessment of the situation is excellent. I would just add that in my practice random forests suffer some of the worst miscalibration that I’ve ever witnessed as a statistician. Even a single ...
Frank Harrell's user avatar

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