Predicting Airline Delays at Scale with Spark ML

A UC Berkeley team used Apache Spark ML to predict airline delays at scale, training models on millions of flight records and ...
Unite.AI

How to Build Reliable RAG: A Deep Dive into 7 Failure Points and Evaluation Frameworks

Retrieval-Augmented Generation (RAG) is critical for modern AI architecture, serving as an essential framework for building ...
Morning Overview on MSN

New AI model aims to improve seasonal drought forecasts

Federal scientists announced a new artificial intelligence tool that can forecast drought conditions 90 days ahead across the ...
ascopubs.org

Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States

This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model ...
EurekAlert!

Learning to be simple: machine learning uncovers structures in finite simple groups

A research team of mathematicians and computer scientists has used machine learning to reveal new mathematical structure within the theory of finite groups. By training neural networks to recognise ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent Eligible Machine-Learning Claims

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
SciELO

Comparison of supervised machine learning and variable selection methods for body weight prediction of growth pigs using image processing data

This research aimed to compare statistical methods (random forest, RIDGE, LASSO, and elastic net regression) for the prediction of body weight in purebred and crossbred pigs reared in Brazil. This ...