Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features

1 April 2021

Papers citing "Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features"

7 / 7 papers shown

Title
Scalable Computations for Generalized Mixed Effects Models with Crossed Random Effects Using Krylov Subspace Methods Pascal Kündig Fabio Sigrist 24 0 0 14 May 2025
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods Andrej Tschalzev Paul Nitschke Lukas Kirchdorfer Stefan Lüdtke Christian Bartelt Heiner Stuckenschmidt 41 0 0 01 Jul 2024
Using Machine Learning Methods for Automation of Size Grid Building and Management Salim Yunus Dries F. Benoit Filipa Peleja 18 0 0 16 Jun 2023
Machine Learning with High-Cardinality Categorical Features in Actuarial Applications Benjamin Avanzi G. Taylor Melantha Wang Bernard Wong 22 12 0 30 Jan 2023
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge Eustache Diemert Romain Fabre Alexandre Gilotte Fei Jia Basile Leparmentier Jérémie Mary Zhonghua Qu Ugo Tanielian Hui Yang 59 6 0 31 Jan 2022
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 85 455 0 13 Jul 2021
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 113 2,735 0 18 Aug 2015