Using feature importance to interpret your models? This paper might be of interest to you. Papers by @gunnark.bsky.social are always worth checking out.

My stock portfolio is deep in the red, and tariffs by the Trump admin might be the cause. Could an LLM have been used to calculate them? It made me rethink how LLMs shape decisions, from big global-economy-wrecking ones to everyday decisions.

SHAP interpretations depend on background data — change the data, change the explanation. A critical but often overlooked issue in model interpretability. Read more:

I recently joined The AI Fundamentalists with my co-author Timo Freiesleben to discuss our book Supervised Machine Learning for Science. We explored how scientists can leverage ML while maintaining rigor and embedding domain knowledge.

The 3rd edition of Interpretable Machine Learning is out! 🎉 Major cleanup, better examples, and new chapters on Data & Models, Interpretability Goals, Ceteris Paribus, and LOFO Importance. The book remains free to read for everyone. But you can also buy ebook or paperback.

Has anyone seen Counterfactual Explanations for machine learning models somewhere in the wild? They are often discussed in research papers, but I have yet to see them being used somewhere in an actual process or product.

Trying Claude Code for some tasks. Paradoxically, it's most expensive when it doesn't work because it fails, then tries a couple of times again, burning through tokens. So sometimes it's 20 cents for saving you 20 minutes of work. Other times it's $1 for wasting 10 minutes.

Only waiting for the print proof, but if it looks good, I'll publish the third edition of Interpretable Machine Learning next week. As always, it was more work than anticipated—especially moving the entire book project from Bookdown to Quarto, which took a bit of effort.

Can an office game outperform machine learning? My most recent post on Mindful Modeler dives into the wisdom of the crowds and prediction markets. Read the full story here:

A year ago, I took a risk & spent quite some time on a ML competition. It paid off—I won 4th place overall & 1st in explainability! Here's a summary of the journey, challenges, & key insights from my winning solution (water supply forecasting)

OpenAI right now

The original SHAP paper has been cited over 30k times. The paper showed that attribution methods, like LIME and LRP, compute Shapley values (with some adaptations). The paper also introduces estimation methods for Shapley values, like KernelSHAP, which today is deprecated.

To this day, the Interpretable Machine Learning book is still my most impactful project. But as time went on, I dreaded working on it. Fortunately, I found the motivation again and I'm working on the 3rd edition. 😁 Read more here:

How I sometimes feel working on "traditional" machine learning topics instead of generative AI stuff 😂

It's quite ironic how people who built the best prediction models are such bad predictors themselves. They throw all their knowledge about how to make good predictions overboard and just claim things like: AI will replace radiologists in a few years or when they expect AGI.

The problem with all these AI demos (especially for image and video generation): They are the most impressive, cherry-picked examples. That includes cherry-picking prompts and themes that produced better results. But as a user, you want good results for every prompt/theme relevant to your use case

Looking for a Christmas gift for a stubborn Bayesian or an over-hyped AI enthusiast? Modeling Mindsets is a short read to broaden your perspective on data modeling. christophmolnar.com/books/modeli... *Hat not included.

My personal rules for AI-assisted writing: • Use AI only for small and specific stuff, like grammar fixes or making suggestions for factual corrections. • Never let an LLM change voice and tone. • I review any changes made by AI.

What a sad timeline, where vaccines — one of medicine's clearest wins with all upside and minimal downside — have become targets. Can't we have like an anti-knee arthroscopy movement or whatever instead?

Citing a non-deterministic, "hallucinating", and non-reproducible LLM output is wild. While the norms and best practices are evolving, citing them seems the wrong way. (even wilder when some people add "ChatGPT" as their co-authors)

What are shapley interactions and why should you care about them? This is a guest post by Julia, Max, Fabian and Hubert on my newsletter Mindful Modeler. I also learned a lot from this post and definitely recommend checking out the shapiq package. mindfulmodeler.substack.com/p/what-are-s...

Is anyone aware of a completely AI-generated book that people actually read? Excluding books that are dedicated "AI experiments" and where the book is more about the experiment. Also excluding AI-assisted books where generative AI played a minor role

The unofficial GIF-based pandas library documentation. pandas.DataFrame.rolling

Without non-linear activation functions, neural networks would be linear models, no matter how many layers are stacked.

Got myself a Samsung Galaxy S9 tablet for note-taking, and I love it. (and yes, I'm reading my own book here as a reference for another project, feeling like an imposter because I don't have everything memorized 😂)

I'm always amazed at how popular the random forest algorithm is for remote sensing research. I would have expected deep learning to be more popular there (not to say it isn't). Must be an attraction to trees. 😁

What's the difference between explainability and interpretability? Does the machine learning community have an agreed-upon definition? No. There's a great overview, which is from this paper: arxiv.org/abs/2211.08943 My take: I prefer interpretability since the term explainability is too strong.

AI for science could be more impactful than chatbots. It is already helping win Nobel prizes and accelerating drug development and materials discovery. Today we published an essay about it: why it matters, how it’s happening and its implications. Here is a summary from an econ / social sci lens.

Machine learning and statistics have very narrow ideas of what a model is and how to abstract the world. To broaden the "model" horizon, I can recommend these two books: • Thinking in Systems by Donella H. Meadows • Simulation and Similarity by Michael Weisberg (ignore the monkey)

No one can explain stochastic gradient descent better than this panda.

Looking for a nice classification dataset? There's a cute alternative to Iris: The Palmer penguins dataset Find the data here: github.com/allisonhorst... Artwork by @allisonhorst.bsky.social

I feel like not enough people know about Quarto for creating documents. How it works: Write in markdown and use Quarto to convert it to html, pdf, epub, ... I produce my books with Quarto (web + ebook + print version). But you can also use it for websites, reports, dashboards, ... quarto.org

Writing my Master's thesis: "I hate writing." Starting my first book: "Writing is fun! Maybe I should do a PhD." During my PhD: "I hate writing." Turns out, I hate writing papers but love writing books. Glad I made the career jump from academia to writer!

Optimist: the cup is half full Pessimist: the cup is half empty Statistician: The cup's content is a random variable. Let's assume a Beta distribution and I need a few more cups to tell you more about how full they are.

Just realized BlueSky allows sharing valuable stuff cause it doesn't punish links. 🤩 Let's start with "What are embeddings" by @vickiboykis.com The book is a great summary of embeddings, from history to modern approaches. The best part: it's free. Link: vickiboykis.com/what_are_emb...

More and more ML people are now posting on Twitter about switching to Bluesky, and these last few days, Bluesky was my number one social media platform (sorry, LinkedIn). Really feels like a tipping point (at least for my bubble).

That's the way. Consistency is overhyped. The irregularities keep the coder alert and creative.

New here? Interested in AI/ML? Check out these great starter packs! AI: go.bsky.app/SipA7it RL: go.bsky.app/3WPHcHg Women in AI: go.bsky.app/LaGDpqg NLP: go.bsky.app/SngwGeS AI and news: go.bsky.app/5sFqVNS You can also search all starter packs here: blueskydirectory.com/starter-pack...

Our latest recommendations for reporting #AI/#machinelearning prediction model studies in healthcare are available in the @bmj.com bmj.com/content/385/... Don't forget the further guidance for each reporting recommendation in the supplementary material #MLSky #statsSky #reportingstandards

The first list filled up, so here's a second list of AI for Science researchers on bluesky. Let me know if I missed you / if you'd like to join! bsky.app/starter-pack...

Even as an interpretable ML researcher, I wasn't sure what to make of Mechanistic Interpretability, which seemed to come out of nowhere not too long ago. But then I found the paper "Mechanistic?" by @nsaphra.bsky.social and @sarah-nlp.bsky.social, which clarified things.

After years of writing with Vim, it's finally time to write my books in a modern editor. Time to enter the 21st century of editors. A new era of writing. A new me.

As a machine learning community, we are obsessed with benchmarks. A short rant in the latest issue of Mindful Modeler:

I love random forests

That's such a simple yet great idea for a reference book for any machine learning person. The first pages look very promising, I love the visuals for the metrics:

Had a discussion with a Causal ML researcher yesterday. They have trouble publishing because reviewers always want benchmarks, which don't make sense for many causal questions. I had the same troubles with publishing interpretability research.

Interested in machine learning in science? Timo and I recently published a book, and even if you are not a scientist, you'll find useful overviews of topics like causality and robustness. The best part is that you can read it for free: ml-science-book.com

A starter pack of people working on interpretability / explainability of all kinds, using theoretical and/or empirical approaches. Reply or DM if you want to be added, and help me reach others! go.bsky.app/DZv6TSS

These starter packs were super helpful to connect with the ML/AI community. Highly recommended:

Working on the 3rd edition of Interpretable Machine Learning. My first approach was to just add a few chapters, update some sections. As I go deeper, I realize I go fully into "rewrite" mode. Had to add a reminder not to rewrite the entire book 😂