Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions

Found 34 related Books

You are about to access Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions.Access Speed for this file: 34091 KB/Sec


Free Membership Registration to Download

Our library can be accessed from certain countries only.

Please, see if you are eligible to read or download Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions by creating an account.

You must create a free account in order to read or download this book.

Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions by Giovanni Seni.pdf

Uploaded : 2018/05/23 

Last checked : 48 Minutes ago!

User rating : 5 / 4
 Downloads : 8570



Descriptions : 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 br br 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 br br 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 br br Table of Contents Ensembles Discovered Predictive Learning and Decision Trees Model Complexity Model Selection and Regularization Importance Sampling and the Classic Ensemble Methods Rule Ensembles and Interpretation Statistics Ensemble Complexity

9182 Users Online

9182 Users Online