AI models explain 17% of earnings-day stock moves, up from 5%

To explain the stock market's reaction to earnings results, investors and other market participants have used a standard tool ...
Health Affairs

The Trillion-Dollar Algorithm: Lessons From Machine Learning For MA Risk Adjustment

The current MA risk adjustment model has shortcomings, both in predictive accuracy and payment equity across the Medicare ...
eLife

Uncovering genetic mechanisms underlying trait variation in switchgrass using explainable artificial intelligence

The study by Izquierdo and colleagues provides important insights into the field of genomic and transcriptomic prediction of traits across multiple environments. The rationale and analyses conducted ...
PNAS

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Dinosaur footprints are iconic fossils, but it is challenging to identify their makers. This is illustrated by a long-standing debate about whether some footprints from the Late Triassic-Early ...
Hosted on MSN

Bias vs variance explained: Avoid overfitting in ML

What is overfitting and underfitting in machine learning? What is Bias and Variance? Overfitting and Underfitting are two common problems in machine learning and Deep learning. If a model has low ...
Investopedia

Overfitting in Data Modeling: Understanding and Prevention

Alexandra Twin has 15+ years of experience as an editor and writer, covering financial news for public and private companies. Investopedia / Zoe Hansen Overfitting occurs when a model is too closely ...
Business Insider

I quit Amazon for a $400,000 AI job offer from Meta at 22. Here's how I got into the industry and my advice for others.

This as-told-to essay is based on a conversation with Manoj Tumu, a 23-year-old machine learning engineer at Meta based in Menlo Park. It's been edited for length and clarity. Business Insider has ...
IEEE

Machine Learning Evaluation Metric Discrepancies Across Programming Languages and Their Components in Medical Imaging Domains: Need for Standardization

Abstract: This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation in medical ...
holisticai

Enhancing Transparency in AI: Explainability Metrics for Machine Learning Predictions

Artificial Intelligence (AI) has made significant strides in predictive performance, yet it often operates like a “black box” in which its inner workings and the rationale behind its outputs are a ...