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Tim's blog

  • 7 min read

Is XGBoost really all we need?

If you have experience building machine learning models on tabular data you will have experienced that gradient boosting based algorithms like catboost, lightgbm and xgboost are almost always superior.

It's not for nothing Bojan Tunguz (a quadruple kaggle grandmaster employed by Nvidia) states:

... but aren't we all fooling ourselves?

  • 9 min read

Adjusting the bootstrap in Random Forest

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).

  • 4 min read

Reproducible Reports with MkDocs

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.

  • 6 min read

Introducing Skorecard for building better logistic regression models

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.

  • 21 min read

From Central Limit Theorem to Bayes's Theorem via Linear Regression

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.

  • 8 min read

How to develop Python Google Cloud Functions

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.