In this post, I'd like to tell the story of my journey into the open source world of Python with a focus on scikit-learn. My hope is that it encourages others to start or to keep contributing and have endurance for bigger picture changes.

Version 1.0.0 of the new Python package for model-diagnostics was just released on PyPI.

Applied statistics is dominated by the ubiquitous mean. For a change, this post is dedicated to quantiles. I will give my best to provide a good mix of theory and practical examples. While the mean describes only the central tendency of a distribution or random sample, quantiles are able to ...

This post shows how filling histograms can be done in very different ways thereby connecting very different areas: from gradient boosted trees to SQL queries to one-hot encoding. Let's jump into it! Modern gradient boosted trees (GBT) like LightGBM, XGBoost and the HistGradientBoostingRegressor of scikit-learn all use two techniques on ...

In this blog post, I tell the story how I learned about a theorem for random matrices of the two Ukrainian🇺🇦 mathematicians Vladimir Marchenko and Leonid Pastur. It all started with benchmarking least squares solvers in scipy. Setting the Stage for Least Squares Solvers Least squares starts with a matrix ...

TLDR: The number of subsampled features is a main source of randomness and an important parameter in random forests. Mind the different default values across implementations. Randomness in Random Forests Random forests are very popular machine learning models. They are build from easily understandable and well visualizable decision trees and ...

Beyond the least squares TLDR: an illustration of the minimal norm solution in Python.

A look at Swiss mortality for 20 and over 100 years.