regression
regression on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on regression in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around regression, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Catching bugs in scikit-learn [D]
Scikit-learn users, be aware: version 1.9 includes a fix for a subtle bug in the BayesianRidge uncertainty calculation. Keen observers can now explore this firsthand through a fascinating bug-hunting exercise. The provided notebook [https://github.com/aadya940/scikit-verify/blob/master/examples/sklearn_bug_hunting.ipynb] challenges you to identify the formula change between versions 1.8 and 1.9 before revealing the solution. For those seeking to maximize their coding agent efficiency, consider "How to Effectively Solve 100+ Tasks with Claude Code" for deeper insights.
Why does lightgbm not fit my toy example but catboost does? (2 order interactions) [D]
Understanding how tree-based models handle interaction effects can be surprisingly complex. This exploration highlights a peculiar scenario: LightGBM struggles to fit a simple toy example exhibiting a two-way interaction, while CatBoost achieves a perfect fit. The dataset, designed with equal target means across explanatory variable values and a clear interaction variable ("AB"), reveals LightGBM's inability to capture the relationship. This prompts a crucial question: what differentiates CatBoost’s approach—does it prioritize more aggressive splitting—allowing it to model interactions effectively, even without explicit interaction terms?
My Model Was Cheating on Its Own Test
Data scientists often strive for model accuracy, but what happens when a model gains an unfair advantage? In a recent *Towards Data Science* post, an author discovered their car price prediction model was "cheating" – a preprocessing pipeline inadvertently allowed it to glimpse the test set. This resulted in a deceptively high R-squared score. The experience highlights a critical pitfall in machine learning workflows and the importance of rigorous validation.
Introduction to Semi-Supervised Learning
## Introduction to Semi-Supervised Learning Semi-supervised learning offers a powerful bridge between supervised and unsupervised techniques, leveraging both labeled and unlabeled data to build more robust models. This primer explores the core concepts, detailing common algorithmic approaches—from self-training to graph-based methods—and their practical applications. While utilizing unlabeled data can significantly enhance performance, it's crucial to acknowledge inherent limitations; biases in the unlabeled set can propagate, impacting model accuracy.
![Deep learning tackles single-cell analysis – A survey of deep learning for scRNA-seq analysis [R]](https://preview.redd.it/n3okgq66t1eh1.png?width=140&height=99&auto=webp&s=c7f944d68ce877e0198147bb832e40cbb826fa91)
Deep learning tackles single-cell analysis – A survey of deep learning for scRNA-seq analysis [R]
Navigating the complexities of single-cell RNA sequencing (scRNA-seq) analysis demands sophisticated tools. A recent survey paper, "Deep learning tackles single-cell analysis," comprehensively examines 25 distinct deep learning methods across six key subcategories. To aid understanding, one user has meticulously summarized these approaches, detailing their purpose, architecture, metrics, and novelty within a readily accessible table.