Supervised Machine Learning: Optimization Framework and Applications With SAS and R - Couverture rigide

Kolosova, Tanya; Berestizhevsky, Samuel

Couverture rigide

ISBN 10 : 0367277328 ISBN 13 : 9780367277321

Editeur : Chapman & Hall/CRC, 2020

Synopsis

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.

A propos du livre

À propos de l?auteur

Tanya Kolosova is a statistician, software engineer, an educator, and a co-author of two books on statistical analysis and metadata-based applications development using SAS. Tanya is an actionable analytics expert, she has extensive knowledge of software development methods and technologies, artificial intelligence methods and algorithms, and statistically designed experiments.

Samuel Berestizhevsky is a statistician, researcher and software engineer. Together with Tanya, Samuel co-authored two books on statistical analysis and metadata-based applications development using SAS. Samuel is an innovator and an expert in the area of automated actionable analytics and artificial intelligence solutions. His extensive knowledge of software development methods, technologies and algorithms allows him to develop solutions on the cutting edge of science.

Détails de l'édition

• Éditeur: Chapman & Hall/CRC
• Date d'édition: 2020
• Langue: anglais
• ISBN 10: 0367277328
• ISBN 13: 9780367277321
• Reliure: Relié
• Nombre de pages: 160
• Coordonnées du fabricant: non disponible