introduction classifier performance analysis de saw sutaip (44 résultats)

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Paperback. Etat : New. Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features:An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learningCommonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPALimitations of some commonly used performance measures are highlightedCoverage includes performance parameters and inferential techniques for themAlso covered are techniques for comparative analysis of competing classifiersA key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick packageThis is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.

Etat : New. In.

Paperback. Etat : new. Paperback. Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features:An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learningCommonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPALimitations of some commonly used performance measures are highlightedCoverage includes performance parameters and inferential techniques for themAlso covered are techniques for comparative analysis of competing classifiersA key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick packageThis is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA. This book provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

paperback. Etat : New.

Paperback / softback. Etat : New. New copy - Usually dispatched within 4 working days. 410.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : New. Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features:An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learningCommonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPALimitations of some commonly used performance measures are highlightedCoverage includes performance parameters and inferential techniques for themAlso covered are techniques for comparative analysis of competing classifiersA key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick packageThis is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.

Paperback. Etat : Brand New. 224 pages. 9.18x6.12x9.21 inches. In Stock.

Etat : New. 2024. 1st Edition. paperback. . . . . .

Etat : New.

Etat : New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Etat : Brand New. 224 pages. 9.18x6.12x9.21 inches. In Stock.

Paperback. Etat : new. Paperback. Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features:An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learningCommonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPALimitations of some commonly used performance measures are highlightedCoverage includes performance parameters and inferential techniques for themAlso covered are techniques for comparative analysis of competing classifiersA key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick packageThis is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA. This book provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Etat : New. Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features:An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learningCommonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPALimitations of some commonly used performance measures are highlightedCoverage includes performance parameters and inferential techniques for themAlso covered are techniques for comparative analysis of competing classifiersA key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick packageThis is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.

hardcover. Etat : Very Good.

Paperback. Etat : New. Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features:An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learningCommonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPALimitations of some commonly used performance measures are highlightedCoverage includes performance parameters and inferential techniques for themAlso covered are techniques for comparative analysis of competing classifiersA key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick packageThis is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.

Taschenbuch. Etat : Neu. Introduction to Classifier Performance Analysis with R | Sutaip L. C. Saw | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | Chapman and Hall/CRC | EAN 9781032850108 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Etat : New.

Hardback. Etat : New. New copy - Usually dispatched within 4 working days. 571.