Machine Learning for Knowledge Discovery with R (Record no. 10449)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01987 a2200277 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250526161930.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250430042023xx 196 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781032071596 |
| Qualifying information | BC |
| 037 ## - SOURCE OF ACQUISITION | |
| Source of stock number/acquisition | Taylor & Francis |
| Terms of availability | GBP 45.99 |
| Form of issue | BB |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | 01 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PBT |
| Source | thema |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | TJFM |
| Source | thema |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PBT |
| Source | bic |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | TJFM |
| Source | bic |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | BUS061000 |
| Source | bisac |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | COM037000 |
| Source | bisac |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | MAT029000 |
| Source | bisac |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | 006.312 |
| Source | bisac |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kao-Tai Tsai |
| 9 (RLIN) | 693 |
| 245 10 - TITLE STATEMENT | |
| Title | Machine Learning for Knowledge Discovery with R |
| Remainder of title | Methodologies for Modeling, Inference and Prediction |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Name of publisher, distributor, etc. | Chapman and Hall/CRC |
| Date of publication, distribution, etc. | 20230925 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 244 p |
| 520 ## - SUMMARY, ETC. | |
| Expansion of summary note | Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein. Key Features: Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies. Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations. Written by statistical data analysis practitioner for practitioners. The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications. |
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