Read more in my latest blog post: mindfulmodeler.substack.com/p/whos-reall...

🎧 Listen here: www.buzzsprout.com/2186686/epis...

Part 2 drops soon—stay tuned!

I've found the Quarto documentation to be really good, especially the search: quarto.org

It's still hard to predict for me when it fails. For example, told it to simply check for placements of citations in a markdown file, which should be doable with a regex. And Claude failed. But a similar task worked out the other day.

5/ It was stressful, but I don’t regret it. I learned a lot and definitely feel validated in my skills again.

Full story & solution details: https://buff.ly/4gHZYHD

4/ Writing Supervised ML for Science at the same time was a huge plus—competition & book writing fed into each other (e.g., uncertainty quantification).

3/ One key insight: SHAP’s reference data matters! I used historical forecasts for interpretability. Also combined SHAP with ceteris paribus profiles for sensitivity analysis.

2/ My approach:
✅ XGBoost ensemble, quantile loss
✅ SHAP for explainability + custom waterfall plots + ceteris paribus plots
✅ Conformal prediction to fix interval coverage
✅ Auto-generated reports with Quarto

1/ Years ago, I went full-time into writing & cut ML practice. At some point, I felt like an impostor writing about ML but no longer practicing. This competition about water supply forecasting on DrivenData (500k prize pool) was a way back in.

deprecated was maybe the wrong word. It's no longer the default in the shap package. There are faster alternatives

The connection between SHAP and LIME is only when we represent features differently for LIME and use a different weight function.
My take is that, while interesting, it can be misleading as SHAP and original LIME are very different, as you also say.

Not planned so far