ascopubs.org

Using machine learning to predict mortality in older patients with cancer: Decision tree and random forest analyses from the ELCAPA and ONCODAGE prospective cohorts.

Prediction of treatment (tx)-induced fatigue in breast cancer (BC) patients (pts) using machine learning on genome-wide association (GWAS) data in the prospective CANTO cohort. This is an ASCO Meeting ...

What is a ‘random forest’?

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

Reports from University of Minnesota Describe Recent Advances in Health Insurance (Comparison and Analysis of the Effectiveness of Linear Regression, Decision Tree, and Random ...

2024 MAY 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily-- Fresh data on health insurance are presented in a new report. According to news originating from the ...
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 ...
Visual Studio Magazine

Decision Tree Regression from Scratch with Pointers Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...
Visual Studio Magazine

How to Create a Machine Learning Decision Tree Classifier Using C#

After earlier explaining how to compute disorder and split data in his exploration of machine learning decision tree classifiers, resident data scientist Dr. James McCaffrey of Microsoft Research now ...