Supervised Machine Learning

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing data sets, design and analysis of statistical experiments and optimal hyper-parameters for ML methods.

• | Author: Tanya Kolosova|Samuel Berestizhevsky
• | Publisher: Chapman and Hall/CRC
• | Publication Date: Apr 29, 2022
• | Number of Pages: 182 pages
• | Language: English
• | Binding: Paperback
• | ISBN-10: 0367538822
• | ISBN-13: 9780367538828