Sift Science Blog

> Why is it an issue to mention someone's nationality? It's not an issue to mention someone's nationality. Intent matters. That is why you don't say "he's just a black murderer from NYC" or "he's just an arab terrorist". > Do you have perhaps have any pre-conceived ideas around that particular nationality? No, it seems that you have. What a lame attempt to spin it around. Funny how you edited your post after people called foul, this is your original post: http://i.imgur.com/HaejrYq.png. This link you posted is amusing: http://blog.siftscience.com/2013/global-e-commerce-fraud. Those American scammers!

Nice post. However, I was kind of surprised to see the author list interpretability as one of the drawbacks of random forests: > Another point that some might find a concern is that random forest models are black boxes that are very hard to interpret. I generally agree that random forests are more difficult to interpret than linear models such as logistic regression, but I think they're still far more interpretable than more comparable non-linear models such as neural networks or SVMs with non-linear kernels. At the end of the day, a random forest is just a bunch of decision trees, each of which are very straightforward for humans to understand. Additionally, there are a number of straightforward methods available for assessing the importance of each individual feature in a random forest in aggregate ([1], section 15.4). Neural networks, on the other hand, result in models that are too cryptic to be meaningfully inspected by a human, and have more complex variable importance measures [2]. The relative interpretability of these non-linear models played a large factor in our decision to add random forests to our modeling stack at Sift Science, which you can read more about here: http://blog.siftscience.com/blog/2015/large-scale-decision-forests-les… [1]: http://statweb.stanford.edu/~tibs/ElemStatLearn/ [2]: http://www.massey.ac.nz/~mkjoy/pdf/Olden,Joy&DeathEM.pdf