While reading Dr. Nina Zumel’s excellent note on bias in common ensemble methods, I ran the examples to see the effects she described (and I think it is very important that she is establishing the issue, prior to discussing mitigation). In doing that I ran into one more avoidable but strange […]
In our previous article , we showed that generalized linear models are unbiased, or calibrated: they preserve the conditional expectations and rollups of the training data. A calibrated model is important in many applications, particularly when financial data is involved. However, when making predictions on individuals, a biased model may […]
We have always regretted that we didn’t get to cover gradient boosting in Practical Data Science with R (Manning 2014). To try make up for that we are sharing (for free) our GBM lecture from our (paid) video course Introduction to Data Science. (link, all support material here). Please help […]
Research surveys tend to fall on either end of the spectrum: either they are so high level and cursory in their treatment that they are useful only as a dictionary of terms in the field, or they are so deep and terse that the discussion can only be followed by […]