The RandomForest algorithm was introduced by Breiman back in 2001 (paper). In 2022 it is still a commonly used algorithm by many data scientists. The only difference is that the current scikit-learn implementation combines classifiers by averaging their probabilistic prediction, instead of letting each classifier vote for a single class (source).
In the post Using MkDocs for technical reporting I explained how MkDocs works and why it's a good choice for writing technical reports.
In this post I'll explain how to work with different MkDocs plugins to make your documentation more reproducible. I find the topic exciting as the combination of these plugins is especially powerful. That's also why I wrote multiple MkDocs plugins and contributed to many more to make the workflow even smoother.
skorecard is an open source python package that provides scikit-learn compatible tools for bucketing categorical and numerical features and building traditional credit risk acceptance models (scorecards) on top of them. These tools have applications outside of the context of scorecards and this blogpost will show you how to use them to potentially improve your own machine learning models.
In this post I will explain why MkDocs is well suited for writing technical reports on machine learning models and introduce relevant parts of the MkDocs ecosystem.
In this post I will demonstrate how to use streamlit to build an app that can help interactively set the threshold for your machine learning classifier. See also the code and the live demo app.
Take any statistics course and you'll have heard about the central limit theorem. And you might have read about Bayes' theorem offering a different, more probabilistic method. In this long post I'll show how they are related, explaining concepts such as linear regression along the way. I'll use math, history, code, examples and plots to show you why both theorems are still very relevant for modern data scientists.
This post will demonstrate how to quickly find for a given coordinate (latitude and longitude), the closest coordinate out of a list of other coordinates using K-D-Trees and the Euclidean distance.
I've been using Google's Cloud Functions for a project recently. My use case was building a small webscraper that periodically downloads excel files from a list of websites, parses them and write the structured data into a BigQuery database. In this blogpost I'll show you why going serverless with cloud functions is so amazing and cost-effective. And I'll discuss some practical problems that have solutions you can't easily find in the documentation or stackoverflow.