Christoph Molnar 🦋 christophmolnar.bsky.social

Exciting news!

Our book Supervised Machine Learning for Science is now published! 🥳🥳🥳

Available in hardcover, paperback, eBook, and PDF. You can even read it online for free!

The trouble with uncertainty quantification in ML is a lack of guarantee: prediction intervals that are too short and class probabilities are miscalibrated.

A solution: conformal prediction.

To get started, I wrote a beginner-friendly, hands-on book.

shap is probably the most popular machine learning interpretation method.

shap is versatile: it works for different ML models, input data, and tasks.

But shap also comes with many caveats. That's why I wrote
"Interpreting Machine Learning Models With SHAP".

bayesian or frequentist?
stats or ml?
supervised or unsupervised?
association or causation?

Math and methods overlap, but mindsets differ. We often focus on teaching technicalities and the big picture falls short.

That motivated me to write Modeling Mindsets, a big-picture book

Even today a perceived barrier to machine learning is the lack of interpretability.

But compared to 10 years ago, we have way more tools for interpreting machine learning models.

The easiest way to get started is my book Interpretable Machine Learning: