Mindful Modeler

I quit writing Building Blocks of ML

And lost 3 months of work

Dec 3 • 

Christoph Molnar

18

3

2

November 2025

Making classifier evaluation less confusing

A short guide through the metric jungle: ROC AUC, F1, log loss and more.

Nov 25

21

2

2

Points, Rules, Weights, Distributions: The Elements of Machine Learning

How all (?) ML models boil down to 4 learnable elements

Nov 6 • 

Christoph Molnar

20

1

2

October 2025

How a Simple Formula Sparked My Next Book

Breaking machine learning algorithms into their fundamental building blocks

Oct 22 • 

Christoph Molnar

19

2

1

September 2025

6 Things I Hate About SHAP as a Maintainer

A guest post by Tobias Pitters

Sep 24 • 

Christoph Molnar

22

3

From Frequencies to Coverage: Rethinking What “Representative” Means

From Statistical Distributions to ImageNet’s 1,000 Abaci

Sep 3 • 

Christoph Molnar

12

2

July 2025

Causal Explanations: A Formal Way to Actually Understand Why

A guest post by David A. Kelly, Nathan Blake, and Hana Chockler (King’s College London)

Jul 15 • 

Christoph Molnar

22

Working with Satellite Data? Let’s talk.

I’m writing a hands-on book for ML folks getting into remote sensing.

Jul 4 • 

Christoph Molnar

21

May 2025

Additive Thinking: A Human Habit

A guest post by Vasilis and Julia

May 8 • 

Christoph Molnar

 and 

Vasilis Gkolemis

29

April 2025

Who’s Really Making the Decisions?

LLMs, tariffs, and the silent takeover of decisions

Apr 8 • 

Christoph Molnar

21

1

SHAP Interpretations Depend on Background Data — Here’s Why

Or why height doesn't matter in the NBA

Apr 1 • 

Christoph Molnar

42

1

March 2025

The 3rd Edition of Interpretable ML is published 🥳

I published the third edition of the Interpretable Machine Learning book.

Mar 13 • 

Christoph Molnar

28

5

2