Supervised Machine Learning: Optimization Framework and Applications with SAS and R-P2P
AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers.

Supervised Machine Learning: Optimization Framework and Applications with SAS and R-P2P
English | 2021 | ISBN-13: 978-0367277321 | 176 Pages | True (PDF, EPUB) | 8.08 MB
NITROFLARE – RAPIDGATOR – NTi

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