This package provides two classes - one for evaluating the merit of individual attributes using a classifier (ClassifierAttributeEval), and second for evaluating the merit of subsets of attributes using a classifier (ClassifierSubsetEval). Both invoke a user-specified classifier to perform the evaluation, either under cross-validation or on the training data.
Related Books Machine Learning Algorithms in Depth (2025) by Smolyakov, Vadim Ultimate Java for Data Analytics and Machine Learning: Unlock Java's Ecosystem for Data Analysis and Machine Learning Using WEKA, JavaML, JFreeChart, and Deeplearning4j (English Edition) (2024) by Kumar, Abhishek Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud (2023) by Tranquillin, Marco, Lakshmanan, Valliappa, Tekiner, Firat Machine Learning System Design Interview (2023) by Aminian, Ali, Xu, Alex Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically (2022) by Prosise, Jeff Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (2022) by Huyen, Chip Machine Learning Engineering in Action (2022) by Wilson, Ben Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps (2020) by Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition (2018) by Bhatia, AshishSingh, Kaluza, Bostjan