電気通信システムと管理

Quantifying uncertainty in machine learning models

Abstract

Samuel Rochette

We'll see why and how it is very important to compute uncertainty in inferential statistics and predictive machine learning models.
1) Deep dive in random forest
Random Forest gives us naturally an estimation of the distribution for each sample thanks to the bagging technic.
2) Generalisation for regression
The quantile loss is useful to compute prediction intervals for every regression model. It is however a computationally costly. Certain loss like cosh can help against this con.
3) What about classification
In classification, probability is a measure of the uncertainty... but does every models give us good probabilities ? Let plot some reliability curve to check if we need to calibrate the output with a sigmoid a an isotonic regression !