In two of my previous blog posts, I explained how the black box of a random forest can be opened up by tracking decision paths along the trees and computing feature contributions. This way, any prediction can be decomposed into … Continue reading
Today, we had the first event of the Estonian Machine Learning Meetup series. I was quite baffled by the pretty massive turnout, with more than a hundred people attending, indicating that such an event series is long overdue. So props … Continue reading
In one of my previous posts I discussed how random forests can be turned into a “white box”, such that each prediction is decomposed into a sum of contributions from each feature i.e. .I’ve a had quite a few requests … Continue reading
An aspect that is important but often overlooked in applied machine learning is intervals for predictions, be it confidence or prediction intervals. For classification tasks, beginning practitioners quite often conflate probability with confidence: probability of 0.5 is taken to mean … Continue reading
Why model interpretation?Imagine a situation where a credit card company has built a fraud detection model using a random forest. The model can classify every transaction as either valid or fraudulent, based on a large number of features. What if, … Continue reading