MRmratsim·Jan 12, 2020
Uncertainty is absolutely key everywhere decision making has an impact on human lives.
Detecting if we see an expresso or a cappucino, sure fine.
But uncertainty on a CT scan would be extremely valuable just to avoid people (even trained professionals) falling into the common bias of trusting the output.
Say the model outputs 90% chance of nothing, 10% chance of a deadly condition that needs to be investigated ASAP. Doctors are often overworked and tired and can miss it. Knowing where the certainties and uncertainties of the model lies would allow the doctor to compare that to its own training "Ah but it didn't at all judge based on X and Y" and limit decision machine based on "trusting the algorithms".
This is a similar problem that rating system based on just a number of stars are facing, you need to read the comments and the why just t
Read the rest
o filter out astroturfing.
Coming back to ML, this is why I think more emphasis should be given to LIME [1], SHAP [2] and also Microsoft's Explainable Boosting Machine [3] which ironically is not a gradient boosting algorithm but a Generalized Additive Model (i.e. like GLM but Squared instead of Linear)
[1] https://github.com/marcotcr/lime
[2] https://github.com/slundberg/shap
[3] https://github.com/interpretml/interpret