:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Dolgopolyi, Roman, Amaslidou, Ioanna, Margaritou, Agrippina
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.00542
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

EEG Emotion Recognition Through Deep Learning
by: Dolgopolyi, Roman, et al.
Published: (2025)

Surrogate Interpretable Graph for Random Decision Forests
by: Dubey, Akshat, et al.
Published: (2025)

Unity Forests: Improving Interaction Modelling and Interpretability in Random Forests
by: Hornung, Roman, et al.
Published: (2026)

Mixed-Curvature Decision Trees and Random Forests
by: Chlenski, Philippe, et al.
Published: (2024)

Interpretable Quantile Regression by Optimal Decision Trees
by: Lemaire, Valentin, et al.
Published: (2026)

Learning Interpretable Characteristic Kernels via Decision Forests
by: Panda, Sambit, et al.
Published: (2018)

Online Subsidy Management System Using Machine Learning (Algorithm- Logistic Regression, Random Forest, Decision Tree)
by: Aniket Bhandare, Soundrya Biradar, Nikhil Lonari, Vishwaraj Pawar, Pallavee Bavane-Patil
Published: (2026)

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
by: Agarwal, Abhineet, et al.
Published: (2023)

Random Forest Weighted Local Fréchet Regression with Random Objects
by: Qiu, Rui, et al.
Published: (2022)

Interpretable Network-assisted Random Forest+
by: Tang, Tiffany M., et al.
Published: (2025)

Learning Hyperplane Tree: A Piecewise Linear and Fully Interpretable Decision-making Framework
by: Li, Hongyi, et al.
Published: (2025)

Learning Decision Trees and Forests with Algorithmic Recourse
by: Kanamori, Kentaro, et al.
Published: (2024)

A Federated Random Forest Solution for Secure Distributed Machine Learning
by: Cotorobai, Alexandre, et al.
Published: (2025)

Are Linear Regression Models White Box and Interpretable?
by: Salih, Ahmed M, et al.
Published: (2024)

Interpretable Model-Aware Counterfactual Explanations for Random Forest
by: Harvey, Joshua S., et al.
Published: (2025)

Prediction Error Estimation in Random Forests
by: Krupkin, Ian, et al.
Published: (2023)

Cluster-Based Random Forest Visualization and Interpretation
by: Sondag, Max, et al.
Published: (2025)

A Multi-Head Attention Soft Random Forest for Interpretable Patient No-Show Prediction
by: Amalina, Ninda Nurseha, et al.
Published: (2025)

Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports
by: Liu, Shun
Published: (2024)

Quantile Regression using Random Forest Proximities
by: Li, Mingshu, et al.
Published: (2024)

A Comparative Study of Machine Learning Algorithms for Electricity Price Forecasting with LIME-Based Interpretability
by: Zhao, Xuanyi, et al.
Published: (2025)

The Impact of Bootstrap Sampling Rate on Random Forest Performance in Regression Tasks
by: Iwaniuk, Michał, et al.
Published: (2025)

Random Forest Regression Feature Importance for Climate Impact Pathway Detection
by: Brown, Meredith G. L., et al.
Published: (2024)

A Powerful Random Forest Featuring Linear Extensions (RaFFLE)
by: Raymaekers, Jakob, et al.
Published: (2025)

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
by: Bertsimas, Dimitris, et al.
Published: (2024)

Random Forest-Based Prediction of Stroke Outcome
by: Fernandez-Lozano, Carlos, et al.
Published: (2024)

Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers
by: Hasan, Mahade, et al.
Published: (2025)

A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes
by: Alzboon, Mowafaq Salem, et al.
Published: (2025)

Statistical Advantages of Oblique Randomized Decision Trees and Forests
by: O'Reilly, Eliza
Published: (2024)

Multimodal Machine Learning for Early Prediction of Metastasis in a Swedish Multi-Cancer Cohort
by: Rugolon, Franco, et al.
Published: (2026)

coverforest: Conformal Predictions with Random Forest in Python
by: Meehinkong, Panisara, et al.
Published: (2025)

Prediction via Shapley Value Regression
by: Alkhatib, Amr, et al.
Published: (2025)

A Comparative Study of Diabetes Prediction Based on Lifestyle Factors Using Machine Learning
by: Nguyen, Bruce, et al.
Published: (2025)

Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study
by: Bourchas, Orfeas, et al.
Published: (2026)

Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification
by: Park, Youngjoon
Published: (2026)

Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States
by: Hashtarkhani, Soheil, et al.
Published: (2025)

Optimizing Interpretable Decision Tree Policies for Reinforcement Learning
by: Vos, Daniël, et al.
Published: (2024)

Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
by: Schaeffer, Joachim, et al.
Published: (2024)

On the Tunability of Random Survival Forests Model for Predictive Maintenance
by: Yardımcı, Yigitcan, et al.
Published: (2025)

DynFrs: An Efficient Framework for Machine Unlearning in Random Forest
by: Wang, Shurong, et al.
Published: (2024)