عنوان

Ensemble methods in data mining :

(Number (ISBN

1608452859

(Number (ISBN

9781608452859

Erroneous ISBN

1608452840

Erroneous ISBN

9781608452842

Number

b743445

Title Proper

Ensemble methods in data mining :

General Material Designation

[Book]

Other Title Information

improving accuracy through combining predictions /

First Statement of Responsibility

Giovanni Seni, John F. Elder.

Place of Publication, Distribution, etc.

[San Rafael, Calif.] :

Name of Publisher, Distributor, etc.

Morgan & Claypool Publishers,

Date of Publication, Distribution, etc.

©2010.

Specific Material Designation and Extent of Item

1 online resource (xvi, 108 pages) :

Other Physical Details

illustrations

Series Title

Synthesis lectures on data mining and knowledge discovery,

Volume Designation

#2

ISSN of Series

2151-0075 ;

Text of Note

Includes bibliographical references (pages 101-105).

Text of Note

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

0

Text of Note

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.

Source for Acquisition/Subscription Address

Safari Books Online

Stock Number

CL0500000198

Title

Ensemble methods in data mining.

International Standard Book Number

9781608452842

Data mining-- Mathematical models.

Set theory.

COMPUTERS-- Enterprise Applications-- Business Intelligence Tools.

COMPUTERS-- Intelligence (AI) & Semantics.

Set theory.

COM-- 004000

COM-- 005030

Number

Edition

Class number

Book number

Seni, Giovanni.

Elder, John F., (John Fletcher)

Date of Transaction

20201204035716.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y