What is a ‘random forest’?

Discover how random forests, a machine-learning technique, enhance prediction accuracy by combining insights from multiple ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...
InfoQ

Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques

In this article, the authors discuss how to detect fraud in credit card transactions, using supervised machine learning algorithms (random forest, logistic regression) as well as outlier detection ...
Hackaday

Training Bats In The Random Forest With The Confusion Matrix

When exploring the realm of Machine Learning, it’s always nice to have some real and interesting data to work with. That’s where the bats come in – they’re fascinating animals that emit very ...
Open Heart

Development and validation of a 10-year predictive model for cardiovascular and metabolic disease risk: insights from a large-scale health examination cohort

Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using ...
ProPublica

The (Random) Forests for the Trees: How Our Spillover Model Works

ProPublica is a nonprofit newsroom that investigates abuses of power. Sign up to receive our biggest stories as soon as they’re published. This year at ProPublica, we’ve paired computer modeling with ...
InfoWorld

The engines of AI: Machine learning algorithms explained

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...