R-bloggers

Prediction Intervals for Tabular Regression in Python and R via Conformalized TabICL; comparison with RidgeCV

The manifold hypothesis, the idea that real-world high-dimensional data concentrates near a low-dimensional curved subspace, is foundational to modern machine learning. Many popular manifold learning methods such as UMAP, t-SNE, Isomap, and diff...

1 Introduction Graph databases excel at storing and traversing highly connected data used for recommendation engines, fraud detection, knowledge graphs, and social networks. Neo4j is one of the most widely used graph databases, and with Neo...

Leaflets – CC-BY-NC-ND by Steve Walser Note for myself and others: how to remove the “Leaflet | ” prefix in map attribution using the R package {leaflet}. Note It’s allowed. Masking the attribution is sometimes useful in certain circumstances, but generally please cite the software and data used… According to some sources ...

Introduction rOpenSci’s R-universe system is an open source platform allowing users to create their own CRAN-like universe of R packages. It is absolutely fantastic. It is particularly useful in one area I research, Mendelian randomization (at ...

Prediction Intervals for Tabular Regression in Python and R via Conformalized TabPFN

Exploring the CovR/S two-component system in Group A Strep 🧫 — from genome annotation with Bakta & BaktFold, to AlphaFold confidence metrics, and a first attempt at protein docking with Haddock3. Learning as we go! 🙌 Motivations ...

Examples of use of R package crossvalidation for Probabilistic Time Series Cross-Validation (measuring coverage and Winkler score)

I came across a post recently by a machine learning engineer who made the bold claim that logistic regression is the worst name for an algorithm ever, or something along those lines1. Many statisticians of the more old-school type seemed to disagree. T...

How long do wars last, on average? If a war such as that currently under way in Iran has lasted 74 days so far, how long do we expect it to last in total? For all sorts of reasons, inquiring minds are interested. Luckily there are some very well curate...

A high can make a regression model look impressively accurate — but this number can be deceptive. If you want to understand why a high is not always a sign of a good model, read on! In the post, Learning Data Science: Modelling Basics, we built a simple model to predict ...

News from R package unifiedml, that offers a unified interface to R machine learning models

Great strides in artificial intelligence development during the last five years produced agents that are now commonplace at work and home. It is humbling to note that virtually all frontier large language models today trace back to a preprint introducing the transformer neural network architecture – a fifteen-page paper that profoundly ...

1 Introduction Differencing is one of the most common transformations in time series analysis. It is also one of the easiest transformations to misunderstand. In many ARIMA-style workflows, differencing is introduced almost mechanically: i...

Introduction Differential Machine Learning (DML), as introduced in the recent arXiv paper (Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps), extends supervised learning by incorporating not only function values but also their derivatives. In financial contexts, this often means sensitivities such as Greeks. However, when direct derivatives ...