GitHub

Quantile-Based Prediction using Regression Trees and Forests

Quantile regression forest consistently achieves the lowest pinball loss and effect of split criterion on QRF is minimal. This shows the advantage instability and generalization over single-tree ...
BMJ Open

Development and evaluation of a diagnostic aiding tool for differentiating tropical fevers using artificial intelligence approach: a study protocol from tertiary care hospital ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
IEEE

Enhancing Prediction Accuracy Through Random Forest in Classification and Regression

Abstract: Random Forest is a well-known type of ensemble learning, which combines a number of decision trees to improve the prediction ability and reduce the risk of overfitting. This paper aims at ...
IEEE

High-Precision Modeling of Industrial Process Using Lightweight Deep Forest Regression with Its Application

Abstract: As an amendment to the deep forest, deep forest regression (DFR) has been recently proposed for industrial process modeling. However, training DFR models is an arduous task in terms of ...
Frontiers

Satellite-based monitoring of forage quality in grasslands of the United Kingdom using sentinel-2 data and random forest regression

Introduction: In the temperate grasslands of the UK, forage quality is a key factor influencing both animal performance and environmental impact. Because forage quality strongly affects rumen ...
GitHub

OpenForest4D Multi-Temporal Lidar Processing Pipeline

Example Output: Canopy Height Change Analysis on the Kaibab National Forest, northern Arizona (2012–2019) The figure above illustrates canopy height model change along the Kaibab National Forest in ...