Copertina

Interpretable Machine Learning for Life Expectancy Prediction: A Comparative Study of Linear Regression, Decision Tree, and Random Forest

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Dolgopolyi, Roman, Amaslidou, Ioanna, Margaritou, Agrippina
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Automatic Piecewise Linear Regression for Predicting Student Learning Satisfaction
di: Choi, Haemin, et al.
Pubblicazione: (2025)

Solving Offline Reinforcement Learning with Decision Tree Regression
di: Koirala, Prajwal, et al.
Pubblicazione: (2024)

Prediction via Shapley Value Regression
di: Alkhatib, Amr, et al.
Pubblicazione: (2025)

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