Why prediction-driven variable selection misses confounders and how Bayesian Adjustment for Confounding attempts to fix it. The post Why Your Best Predictive Model Gives the Wrong Treatment Effect appeared first on Towards Data Science .
By Ananya Bhattacharyya
From a chocolate bar with no price tag to a marketing mix model in PyMC, and the 200-year-old integral that stood in between.
The post Why You Think Like a Bayesian but Were Taught Like a Frequentist...
By Spyros Georgopoulos
How Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions The post I Spent an Hour on a Data Preprocessing Task Before Asking Gemini appeared first on Towards Data Science .
By Soner Yıldırım
The article recounts a production incident where a large language model (LLM) was used to evaluate the outputs of another LLM, and the judging model consistently agreed with itself. It explores the implications of relying on one model to assess another’s work, highlighting the potential pitfalls of such an approach. The narrative offers lessons on the limits of trusting automated evaluation systems in real‑world deployments.
By Priyansh Bhardwaj
The article titled "Your AI Bill Is a Toll Booth. Stop Paying Twice." discusses how users are unexpectedly paying more for AI services than anticipated, likening the experience to a toll booth where one pays twice. It highlights the unseen costs that can arise when using AI tools and urges readers to be vigilant about their expenses. The piece was first published on Towards Data Science.
By Gursimar Singh
A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. The post Are Your ML Experiments a Mess?
By Alex Davis