Machine Collaboration https://arxiv.org/abs/2105.02569 We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of base machines for prediction tasks. Unlike bagging/stacking (a parallel & independent framework) and boosting (a sequential 📈🤖

Doubly-robust and heteroscedasticity-aware sample trimming for causal inference http://arxiv.org/abs/2210.10171 A popular method for variance reduction in observational causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This pr 📈🤖

#rstats
The development version of matlib 📦 friendly.r-universe.dev/matlib
now comes with a Viewer pane to see the
rendering of LaTeX matrix operations and equations.

It’s funny how I worked so hard to leave the place I grew up, but I still miss it sometimes.

Okay, let's start using this account with the topic I love the most: Machine Learning.

There's a common mistake in several tutorials on Kaggle and Medium on the linearity in parameters.

Keep this in mind: this assumption IS NOT about looking at scatter plots of y with each of the features!