Machine Learning for Knowledge Discovery With R - Methodologies for Modeling, Inference and Prediction

Informationen zum Autor Kao-Tai Tsai obtained his Ph.D. in Mathematical Statistics from University of California, San Diego and had worked at AT&T Bell Laboratories to conduct statistical research, modelling, and exploratory data analysis. After that, he joined the US FDA and later pharmaceutical companies focusing on biostatistics, clinical trial research and data analysis to address the unmet needs in human health. Klappentext 'Machine Learning for Knowledge Discovery with R' contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes most recent supervised and unsupervised machine learning methodologies Zusammenfassung ‘Machine Learning for Knowledge Discovery with R’ contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes most recent supervised and unsupervised machine learning methodologies Inhaltsverzeichnis 1. Statistical Data Analysis. 2. Examining Data Distribution. 3. Regression with Shrinkage. 4. Recursive Partitioning Modeling. 5. Support Vector Machines. 6. Cluster Analysis. 7. Neural Networks. 8. Causal Inference and Matching. 9. Business and Commercial Data Modeling. 10. Analysis of Response Profiles.

Produktdetails

• Autoren: Kao-Tai Tsai
• Verlag: Taylor & Francis Ltd.
• Sprache: Englisch
• Produktform: Fester Einband
• Erschienen: 24.09.2021
• EAN: 9781032065366
• ISBN: 978-1-0-3206536-6
• Seiten: 244
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Mathematik > Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Sozialwissenschaften, Recht,Wirtschaft > Wirtschaft > Allgemeines, Lexika