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eBook Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (Synthesis Lectures on Data Mining and Knowledge Discovery) download

by John Elder,Robert Grossman,Giovanni Seni

eBook Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (Synthesis Lectures on Data Mining and Knowledge Discovery) download ISBN: 1608452840
Author: John Elder,Robert Grossman,Giovanni Seni
Publisher: Morgan and Claypool Publishers (February 24, 2010)
Language: English
Pages: 126
ePub: 1402 kb
Fb2: 1393 kb
Rating: 4.2
Other formats: txt rtf azw mobi
Category: Technologies
Subcategory: Databases and Big Data

Synthesis Lectures on Data Mining and Knowledge Discovery. Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions Giovanni Seni and John F. Elder 2010

Synthesis Lectures on Data Mining and Knowledge Discovery. Elder 2010. Modeling and Data Mining in Blogosphere Nitin Agarwal and Huan Liu 2009.

This book explained ensemble methods in a very clear manner in only about 100 pages.

Jaffray Woodriff, Quantitative Investment Management. This book explained ensemble methods in a very clear manner in only about 100 pages. But what I hope more is the author can open some MOOC like Coursera or some other books with more detail examples (maybe some examples of Kaggle competition). But overall, this is a must-read book if you are in the data science field.

1 Ensemble Methods n Data Mnng: Improvng Accuracy Through .

1 Ensemble Methods n Data Mnng: Improvng Accuracy Through Combnng Predctons. 2. 3 Synthess Lectures on Data Mnng and Knowledge Dscovery Edtor Robert Grossman, Unversty of Illnos, Chcago Ensemble Methods n Data Mnng: Improvng Accuracy Through Combnng Predctons Govann Sen and John F. Elder 2010 Modelng and Data Mnng n Blogosphere Ntn Agarwal and Huan Lu 2009. 4 Copyrght 2010 by Morgan & Claypool All rghts reserved. Ensembles are useful wth all modelng algorthms, but ths book focuses on decson trees to explan them most clearly.

Giovanni Seni, John Elder. Download (pdf, . 0 Mb) Donate Read. Epub FB2 mobi txt RTF. Converted file can differ from the original. If possible, download the file in its original format.

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. 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. They combine multiple models into one usually more accurate than the best of its components

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly.

Электронная книга "Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions", Giovanni Seni, John Fletcher Elder

Электронная книга "Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions", Giovanni Seni, John Fletcher Elder. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions" для чтения в офлайн-режиме.

Start by marking Ensemble Methods in Data Mining .

Start by marking Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions as Want to Read: Want to Read savin. ant to Read. While there is plenty of valuable information here, a good portion of the book d Ensemble Methods in Data Mining is comprised of six chapters: 1. Ensembles Discovered 2. Predictive Learning and Decision Trees 3. Model Complexity, Model Selection and Regularization 4. Importance Sampling and the Classic Ensemble Methods 5. Rule Ensembles and Interpretation Statistics 6. Ensemble Complexity.

Synthesis Lectures on Data Mining and Knowledge Discovery. Please tell us how we can improve it.

Ensemble Methods in Data Mining : Improving Accuracy Through Combining Predictions. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool. Cancel.

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.
Comments: (7)
Ygglune
An absolutely delightful read! This relatively short book is very well organized. It has excellent examples that including useful snippets of R code. The topics are sequenced are very well. The selection of the material is brilliant. The text really worked for me. I cannot remember the last time I read a scientific book and learnt so much in such a short time. My previous knowledge of ensemble methods was only very shallow (knew a little about most of them and somewhat more about bagging/random forests). But the general theoretical framework of this book really brought clarity into my understanding of ensamble methods. I liked the focus on the context of the methodology rather than a lot of math formulas or too extensive examples. I appreciated that there were not too many unnecessary formulas and unexplained jargon. Highly recommended!
Karg
This book explained ensemble methods in a very clear manner in only about 100 pages. But what I hope more is the author can open some MOOC like Coursera or some other books with more detail examples (maybe some examples of Kaggle competition).

But overall, this is a must-read book if you are in the data science field.
one life
There are very few books available discussing general aspects of ensemble methods. One of them is Ensemble methods from Seni, Elder and Grossmann. It provides a high level overview of ensemble learning. However, the book contains a lot of equations which make it hard to read from the beginning until the end. You will rather pick a few sections and read them independently.

On one side, the book seems rather light for an academic audience (it only surfaces each topic). On the other side, it is too academic for industry practitioners. So it’s not fully clear who the target audience is.

To be noted issues regarding missing axis label on some pictures. Also the quality of certain pictures is really low. In conclusion, I would recommend it only if you need an overview of techniques in the field and are not scared of reading equations instead of plain English.
Wetiwavas
Excellent introduction to Ensemble methods. Good for beginners.
Marelyne
This is a really great (short) book in my opinion. It contains the best "need to know" information found in the Elements in Statistical Learning, and other good books on data mining. The included R code is a big bonus. I am enjoying reading it so far, and I highly recommend it. The only thing that frustrates me is that the online version on the publishers website is in color, while the print version is not. This is the only reason I did not give it 5 stars. I saw the online version first, and thought that the print version would be in color as well. I am sadly mistaken. There are many graphics in this book that reference different colors and it just looks really crappy in grayscale. If you are familiar with the Elements of Statistical Learnining, imagine printing that out in grayscale and you will know what I mean.
Kipabi
Good for study
Carrot
For once, "Product Description" is specific and hype-free. (Apart from the claim regarding importance sampling - dealt with on a single page). This is a concise, to-the-point and accessible introduction to the subject, discussing bagging, random-forest and boosting methods, in classification context. Once these methods are explained, the authors move on to measures of variable importance and model complexity, which may be of less interest to practitioners. R snippets, leveraging rpart and gbm packages, are a plus, but the programming is fairly simple.

PS. Morgan Claypool sell the book's PDF for $20, or $0 for those affiliated with the publisher's institutional subscribers.
This book is an accessible introduction to the theory and practice of ensemble methods in machine learning. It is a quick read, has sufficient detail for a novice to begin experimenting, and copious references for those who are interested in digging deeper. The authors also provide a nice discussion of cross-validation, and their section on regularization techniques is much more straightforward, in my opinion, than the equivalent sections in The Elements of Statistical Learning (Elements is a wonderful, necessary book, but a hard read).

The heart of the text is the chapter on Importance Sampling. The authors frame the classic ensemble methods (bagging, boosting, and random forests) as special cases of the Importance Sampling methodology. This not only clarifies the explanations of each approach, but also provides a principled basis for finding improvements to the original algorithms. They have one of the clearest descriptions of AdaBoost that I've ever read.

The penultimate chapter is on "Rule Ensembles": an attempt at a more interpretable ensemble learner. They also discuss measures for variable importance and interaction strength. The last chapter discusses Generalized Degrees of Freedom as an alternative complexity measure; it is probably of more interest to researchers and mathematicians than to practitioners.

Overall, I found the book clear and concise, with good attention to practical details. I appreciated the snippets of R code and the references to relevant R packages. One minor nitpick: this book has also been published digitally, presumably with color figures. Because the print version is grayscale, some of the color-coded graphs are now illegible. Usually the major points of the figure are clear from the context in the text; still, the color to grayscale conversion is something for future authors in this series to keep in mind.

Recommended.