عنوان

Ensemble methods in data mining :

شابک

1608452859

شابک

9781608452859

شابک اشتباه

1608452840

شابک اشتباه

9781608452842

شماره

b743445

عنوان اصلي

Ensemble methods in data mining :

نام عام مواد

[Book]

ساير اطلاعات عنواني

improving accuracy through combining predictions /

نام نخستين پديدآور

Giovanni Seni, John F. Elder.

محل نشرو پخش و غیره

[San Rafael, Calif.] :

نام ناشر، پخش کننده و غيره

Morgan & Claypool Publishers,

تاریخ نشرو بخش و غیره

©2010.

نام خاص و کميت اثر

1 online resource (xvi, 108 pages) :

ساير جزييات

illustrations

عنوان فروست

Synthesis lectures on data mining and knowledge discovery,

مشخصه جلد

#2

شاپا ي ISSN فروست

2151-0075 ;

متن يادداشت

Includes bibliographical references (pages 101-105).

متن يادداشت

1. Ensembles discovered -- Building ensembles -- Regularization -- Real-world examples: credit scoring + the Netflix challenge -- Organization of this book.

بدون عنوان

0

متن يادداشت

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners - especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques.

منبع سفارش / آدرس اشتراک

Safari Books Online

شماره انبار

CL0500000198

عنوان

Ensemble methods in data mining.

شماره استاندارد بين المللي کتاب و موسيقي

9781608452842

موضوع مستند نشده

Data mining-- Mathematical models.

موضوع مستند نشده

Set theory.

موضوع مستند نشده

COMPUTERS-- Enterprise Applications-- Business Intelligence Tools.

موضوع مستند نشده

COMPUTERS-- Intelligence (AI) & Semantics.

موضوع مستند نشده

Set theory.

موضوع مستند نشده

COM-- 004000

موضوع مستند نشده

COM-- 005030

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Seni, Giovanni.

مستند نام اشخاص تاييد نشده

Elder, John F., (John Fletcher)

تاريخ عمليات

20201204035716.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y