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S-SIRUS: an explainability algorithm for spatial regression Random Forest

• Authors : Patelli, Luca; Golini, Natalia; Ignaccolo, Rosaria

Subjects: Statistics - Machine Learning; Computer Science - Machine Learning

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Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting

• Authors : Juan Carlos Moreno Sánchez; Héctor Gabriel Acosta Mesa; Adrián Trueba Espinosa

Subjects: Support vector regression; Random forest; Extreme gradient boosting, grain yield, vegetation indices, climate data

• Source: Smart Agricultural Technology, Vol 10, Iss , Pp 100791- (2025)

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A path in regression Random Forest looking for spatial dependence: a taxonomy and a systematic review

• Authors : Patelli, Luca; Cameletti, Michela; Golini, Natalia

Subjects: Statistics - Machine Learning; Computer Science - Machine Learning; Statistics - Applications

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Predicting age at onset of childhood obesity using regression, Random Forest, Decision Tree, and K-Nearest Neighbour—A case study in Saudi Arabia.

• Authors : Alanazi, Salem Hamoud^1,2 (AUTHOR) ; Abdollahian, Mali¹ (AUTHOR); Tafakori, Laleh¹ (AUTHOR)

Subjects: *STANDARD deviations; *HEALTH behavior; *ADOLESCENT obesity

• Source: PLoS ONE. 9/26/2024, Vol. 19 Issue 9, p1-21. 21p.

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Perbandingan Prediksi Depresi Mahasiswa dengan Linear Regression, Random Forest, dan Gradient Boosting

• Authors : Ainindzi Nur Meiza Pudjianto; Erwin Yudi Hidayat

Subjects: prediksi; depresi; gradient boosting regression

• Source: SINTECH (Science and Information Technology) Journal, Vol 7, Iss 3 (2024)

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A Comparative Study of Machine Learning Models for Predicting Customer Churn in Retail Banking: Insights from Logistic Regression, Random Forest, GBM, and SVM

• Authors : Md Parvez Ahmed; Md Arif; Abdullah Al Mamun

Subjects: Customer Churn, Retail Banking, Machine Learning, Predictive Modeling, Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), Support Vector Machine (SVM); info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion

• Source: Journal of Computer Science and Technology Studies; Vol. 6 No. 4; 92-101; 2709-104X

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Predicting the potential growth of football players in the EA Sports FC games : a comparative study on logistic regression, random forest and XGBoost

• Authors : Goessens, S.; Goessens, S.

Subjects: cognitive science and artificial intelligence; data science and society; Master thesis

• Source: Master thesis; Degree granted by Tilburg University. TSHD. Cognitive Science and Artificial Intelligence; Supervisor(s): P. Mavromoustakos Blom; ii, 43 p.

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Probabilistic prediction by means of the propagation of response variable uncertainty through a Monte Carlo approach in regression random forest: Application to soil moisture regionalization

• Authors : Ségolène Dega; Peter Dietrich; Martin Schrön

Subjects: uncertainty propagation; probabilistic prediction; Monte Carlo

• Source: Frontiers in Environmental Science, Vol 11 (2023)

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The Application of Ridge Regression, Random Forest and Mean-Variance Model in Portfolio Optimization

• Authors : Lu, Tianyi

• Source: Advances in Economics, Business and Management Research ; Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024) ;

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Land Use Change Modelling Using Logistic Regression, Random Forest and Additive Logistic Regression in Kubu Raya Regency, West Kalimantan

• Authors : Alfa Nugraha Pradana; Anik Djuraidah; Agus Mohamad Soleh

Subjects: land use change modelling; wetlands; logistic regression

• Source: Forum Geografi, Vol 37, Iss 2, Pp 149-163 (2023)

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