Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of...

Full description

Bibliographic Details
Main Authors: Lorena Hafermann, Nadja Klein, Geraldine Rauch, Michael Kammer, Georg Heinze
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Entropy
Subjects:
calibration
machine learning
sparsity
variable selection
Online Access:https://www.mdpi.com/1099-4300/24/6/847

Internet

https://www.mdpi.com/1099-4300/24/6/847

Similar Items