Once upon a time, I was doing some analysis of a random forest. At least I thought it was about the random forest algorithm… I was so wrong. What I was trying to understand and what didn’t feel right was not a random forest; it was the Random Subspace Method. It’s still a forest of…

In my previous post, I explained that I was pondering the rather uneven distribution of features among the trees in a random forest. It didn’t feel right to just verify whether I could achieve a more uniform distribution. I felt compelled to explore how it functions with this wild notion of democracy. Yes, the random…

This post is a bit different. It’s theoretical, without any specific experiments. However, it may still be interesting in considering how some other biological neural networks process new knowledge. This week, there were lectures and materials on various topics around decision trees, bagging, random forests, etc. Time series analysis was also included, but that felt…

Frankenstain is alive. Barely. When learning about popular clustering methods I was wondering that they all are based on points physical proximity. This is a reasonable assumption, but it’s utterly annoying. I am an introvert and I don’t want to be squashed in to one cluster with the nearest bunch of extroverts. So it’s personal.…