It makes complete sense that Random Forests decrease variance by only considering a random subset of features at each split for each individual decision tree, thus leading to uncorrelated trees.

However, what doesn’t make sense to me is — why wouldn’t this increase the bias to the point where the reduction in variance didn’t really matter? If the trees are all biased enough, then it doesn’t matter if variance is removed via averaging — the ensemble will still be bad due to the bias.

Does anyone have intuition as to why the bias isn’t increased too much?

Sorry if this is off-topic.

For researchers for whom English is not their native language, it can be quite difficult to express their thoughts in scientific articles. A high level of English speaking and finished “writing in the science” courses on Coursera are not always enough…

It will be cool if you share the services which are able to help with this.

https://www.youtube.com/watch?v=uZ9CcdBHp-I

Just a project I thought this sub might like! A neural network (no libraries) that starts out with random weights and evolves to create a better and better TRON player!

A recent paper has popped up on my radar that appears (from the title + abstract) to be related to the work in a paper I’m writing. However, it’s published in a fairly low-tier conference that has all proceedings behind a pay-wall. I’ve tried to find it on the authors homepages and also tried to email them for a copy, but they haven’t responded to me. Am I expected to buy the paper for 25 euros, just to see if the content is actually relevant?

Does the machine learning cocktail party algorithm, where two overlapping conversations down a single audio channel can be separated into individual conversations down two separate audio channels, have any effect on capacity in the Shannon Limit?

Our research group made an online quiz to promote our paper recently accepted at the AAAI AIES conference!

Which is more AI? (Quiz)

Defining AI in Policy versus Practice (Paper)

This is a port of Puzer/stylegan-encoder for NVlabs/stylegan2.

https://github.com/rolux/stylegan2encoder