Ensemble Methods. Adapted from slides by Todd Holloway h8p://abeau<fulwww.com/2007/11/23/ ensemble- machine- learning- tutorial/

Save this PDF as:

Size: px
Start display at page:

Transcription

1 Ensemble Methods Adapted from slides by Todd Holloway h8p://abeau<fulwww.com/2007/11/23/ ensemble- machine- learning- tutorial/

2 Outline The NeHlix Prize Success of ensemble methods in the NeHlix Prize Why Ensemble Methods Work Algorithms Bagging Random forests AdaBoost

3 Defini<on Ensemble Classifica.on Aggrega<on of predic<ons of mul<ple classifiers with the goal of improving accuracy.

4 Teaser: How good are ensemble methods? Let s look at the Ne-lix Prize Compe66on

5 Began October 2006 Supervised learning task Training data is a set of users and ra<ngs (1,2,3,4,5 stars) those users have given to movies. Construct a classifier that given a user and an unrated movie, correctly classifies that movie as either 1, 2, 3, 4, or 5 stars \$1 million prize for a 10% improvement over NeHlix s current movie recommender/classifier (MSE = )

6 Just three weeks aaer it began, at least 40 teams had bested the NeHlix classifier. Top teams showed about 5% improvement.

7 However, improvement slowed from h8p://

8 Today, the top team has posted a 8.5% improvement. Ensemble methods are the best performers

9 Rookies Thanks to Paul Harrison's collabora<on, a simple mix of our solu<ons improved our result from 6.31 to 6.75

10 Arek Paterek My approach is to combine the results of many methods (also two- way interac<ons between them) using linear regression on the test set. The best method in my ensemble is regularized SVD with biases, post processed with kernel ridge regression h8p://rainbow.mimuw.edu.pl/~ap/ap_kdd.pdf

11 U of Toronto When the predic<ons of mul.ple RBM models and mul.ple SVD models are linearly combined, we achieve an error rate that is well over 6% be8er than the score of NeHlix s own system. h8p://

13 When Gravity and Dinosaurs Unite Our common team blends the result of team Gravity and team Dinosaur Planet. Might have guessed from the name

14 BellKor / KorBell And, yes, the top team which is from AT&T Our final solu.on (RMSE=0.8712) consists of blending 107 individual results.

15 The winner was an ensemble of ensembles (including BellKor).

16 Some Intui<ons on Why Ensemble Methods Work

17 Intui<ons U<lity of combining diverse, independent opinions in human decision- making Protec<ve Mechanism (e.g. stock porholio diversity) Viola.on of Ockham s Razor Iden<fying the best model requires iden<fying the proper "model complexity" See Domingos, P. Occam s two razors: the sharp and the blunt. KDD

18 Intui<ons Majority vote Suppose we have 5 completely independent classifiers If accuracy is 70% for each 10 (.7^3)(.3^2)+5(.7^4)(.3)+(.7^5) 83.7% majority vote accuracy 101 such classifiers 99.9% majority vote accuracy

19 Strategies Bagging Use different samples of observa<ons and/or predictors (features) of the examples to generate diverse classifiers Aggregate classifiers: average in regression, majority vote in classifica<on Boos<ng Make examples currently misclassified more important (or less, in some cases)

20 Bagging (Construc<ng for Diversity) 1. Use random samples of the examples to construct the classiﬁers 2. Use random feature sets to construct the classiﬁers Random Decision Forests Bagging: Bootstrap Aggrega<on Leo Breiman

21 Bootstrap: consider the following situa<on: A random sample x =(x 1,...,x N ) from unknown probability distribu<on F We wish to es<mate parameter We build es<mate What is the s.d. of ˆθ? ˆθ = s(x) θ = t(f ) Examples: 1) estimate mean and sd of expected prediction error 2) estimate point-wise confidence bands in smoothing

22 Bootstrap: It is completely automa<c Requires no theore<cal calcula<ons Not based on asympto<c results Available regardless of how complicated the es<mator ˆθ

23 Bootstrap algorithm: 1. Select B independent bootstrap samples x 1,...,x B each consis<ng of N data values drawing with replacement from x 2. Evaluate the bootstrap replica<on corresponding to each bootstrap sample ˆθ (b) =s(x b), b=1,...,b 3. Es<mate the standard error of ˆθ using the sample standard error of the B es<mates

24 Bagging: use bootstrap to improve predic6ons 1. Create bootstrap samples, es<mate model from each bootstrap sample 2. Aggregate predic<ons (average if regression, majority vote if classifica<on) This works best when perturbing the training set can cause significant changes in the es<mated model For instance, for least- squares, can show variance is decreased while bias is unchanged

25 Random forests At every level, choose a random subset of the variables (predictors, not examples) and choose the best split among those a8ributes

26 Random forests Let the number of training points be M, and the number of variables in the classifier be N. For each tree, 1. Choose a training set by choosing N <mes with replacement from all N available training cases. 2. For each node, randomly choose m variables on which to base the decision at that node. Calculate the best split based on these.

27 Random forests Grow each tree as deep as possible no pruning! Out- of- bag data can be used to es<mate cross- valida<on error For each training point, get predic<on from averaging trees where point is not included in bootstrap sample Variable importance measures are easy to calculate

28

29 Boos<ng 1. Create a sequence of classifiers, giving higher influence to more accurate classifiers 2. At each itera<on, make examples currently misclassified more important (get larger weight in the construc<on of the next classifier). 3. Then combine classifiers by weighted vote (weight given by classifier accuracy)

30 AdaBoost Algorithm 1. Ini<alize Weights: each case gets the same weight: 2. Construct a classifier using current weights. Compute its error: i w i 3. Get classifier influence, and update example weights α m = log 4. Goto step 2 ε m = w i =1/N, i =1,...,N 1 εm ε m i w i I{y i = g m (x i )} w i w i exp {α m I{y i = g m (x i )}} Final predic<on is weighted vote, with weight α m

31 Classifica.ons (colors) and Weights (size) a[er 1 itera+on Of AdaBoost 3 itera+ons 20 itera+ons from Elder, John. From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods

33 How was the NeHlix prize won? Gradient boosted decision trees Details:

34 Sources David Mease. Sta<s<cal Aspects of Data Mining. Lecture. h8p://video.google.com/videoplay?docid= &q=stats +202+engEDU&total=13&start=0&num=10&so=0&type=search&plindex=8 Die8erich, T. G. Ensemble Learning. In The Handbook of Brain Theory and Neural Networks, Second edi<on, (M.A. Arbib, Ed.), Cambridge, MA: The MIT Press, h8p:// ensemble- learning.ps.gz Elder, John and Seni Giovanni. From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods. KDD 2007 h8p://tutorial. videolectures.net/kdd07_elder_afr/ NeHlix Prize. h8p:// Christopher M. Bishop. Neural Networks for Pa8ern Recogni<on. Oxford University Press

Ensemble Learning Better Predictions Through Diversity. Todd Holloway ETech 2008

Ensemble Learning Better Predictions Through Diversity Todd Holloway ETech 2008 Outline Building a classifier (a tutorial example) Neighbor method Major ideas and challenges in classification Ensembles

CI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore.

CI6227: Data Mining Lesson 11b: Ensemble Learning Sinno Jialin PAN Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Acknowledgements: slides are adapted from the lecture notes

Data Mining Practical Machine Learning Tools and Techniques

Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea

Why Ensembles Win Data Mining Competitions

Why Ensembles Win Data Mining Competitions A Predictive Analytics Center of Excellence (PACE) Tech Talk November 14, 2012 Dean Abbott Abbott Analytics, Inc. Blog: http://abbottanalytics.blogspot.com URL:

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05

Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification

Data Mining. Supervised Methods. Ciro Donalek donalek@astro.caltech.edu. Ay/Bi 199ab: Methods of Computa@onal Sciences hcp://esci101.blogspot.

Data Mining Supervised Methods Ciro Donalek donalek@astro.caltech.edu Supervised Methods Summary Ar@ficial Neural Networks Mul@layer Perceptron Support Vector Machines SoLwares Supervised Models: Supervised

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes

Knowledge Discovery and Data Mining Lecture 19 - Bagging Tom Kelsey School of Computer Science University of St Andrews http://tom.host.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-19-B &

Data Mining. Nonlinear Classification

Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

CS570 Data Mining Classification: Ensemble Methods

CS570 Data Mining Classification: Ensemble Methods Cengiz Günay Dept. Math & CS, Emory University Fall 2013 Some slides courtesy of Han-Kamber-Pei, Tan et al., and Li Xiong Günay (Emory) Classification:

Chapter 11 Boosting. Xiaogang Su Department of Statistics University of Central Florida - 1 -

Chapter 11 Boosting Xiaogang Su Department of Statistics University of Central Florida - 1 - Perturb and Combine (P&C) Methods have been devised to take advantage of the instability of trees to create

Using multiple models: Bagging, Boosting, Ensembles, Forests

Using multiple models: Bagging, Boosting, Ensembles, Forests Bagging Combining predictions from multiple models Different models obtained from bootstrap samples of training data Average predictions or

Data Mining Methods: Applications for Institutional Research

Data Mining Methods: Applications for Institutional Research Nora Galambos, PhD Office of Institutional Research, Planning & Effectiveness Stony Brook University NEAIR Annual Conference Philadelphia 2014

Data Mining Classification: Alternative Techniques. Instance-Based Classifiers. Lecture Notes for Chapter 5. Introduction to Data Mining

Data Mining Classification: Alternative Techniques Instance-Based Classifiers Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar Set of Stored Cases Atr1... AtrN Class A B

Model Combination. 24 Novembre 2009

Model Combination 24 Novembre 2009 Datamining 1 2009-2010 Plan 1 Principles of model combination 2 Resampling methods Bagging Random Forests Boosting 3 Hybrid methods Stacking Generic algorithm for mulistrategy

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Unit # 11 Sajjad Haider Fall 2013 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right

ANALYTICAL TECHNIQUES FOR DATA VISUALIZATION

ANALYTICAL TECHNIQUES FOR DATA VISUALIZATION CSE 537 Ar@ficial Intelligence Professor Anita Wasilewska GROUP 2 TEAM MEMBERS: SAEED BOOR BOOR - 110564337 SHIH- YU TSAI - 110385129 HAN LI 110168054 SOURCES

Leveraging Ensemble Models in SAS Enterprise Miner

ABSTRACT Paper SAS133-2014 Leveraging Ensemble Models in SAS Enterprise Miner Miguel Maldonado, Jared Dean, Wendy Czika, and Susan Haller SAS Institute Inc. Ensemble models combine two or more models to

ECBDL 14: Evolu/onary Computa/on for Big Data and Big Learning Workshop July 13 th, 2014 Big Data Compe//on

ECBDL 14: Evolu/onary Computa/on for Big Data and Big Learning Workshop July 13 th, 2014 Big Data Compe//on Jaume Bacardit jaume.bacardit@ncl.ac.uk The Interdisciplinary Compu/ng and Complex BioSystems

Comparison of Data Mining Techniques used for Financial Data Analysis

Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract

Decision Trees from large Databases: SLIQ

Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values

Data Analytics and Business Intelligence (8696/8697)

http: // togaware. com Copyright 2014, Graham.Williams@togaware.com 1/36 Data Analytics and Business Intelligence (8696/8697) Ensemble Decision Trees Graham.Williams@togaware.com Data Scientist Australian

The Predictive Data Mining Revolution in Scorecards:

January 13, 2013 StatSoft White Paper The Predictive Data Mining Revolution in Scorecards: Accurate Risk Scoring via Ensemble Models Summary Predictive modeling methods, based on machine learning algorithms

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification

Gerry Hobbs, Department of Statistics, West Virginia University

Decision Trees as a Predictive Modeling Method Gerry Hobbs, Department of Statistics, West Virginia University Abstract Predictive modeling has become an important area of interest in tasks such as credit

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Evaluating the Accuracy of a Classifier Holdout, random subsampling, crossvalidation, and the bootstrap are common techniques for

Data Mining Techniques Chapter 6: Decision Trees

Data Mining Techniques Chapter 6: Decision Trees What is a classification decision tree?.......................................... 2 Visualizing decision trees...................................................

Beating the MLB Moneyline

Beating the MLB Moneyline Leland Chen llxchen@stanford.edu Andrew He andu@stanford.edu 1 Abstract Sports forecasting is a challenging task that has similarities to stock market prediction, requiring time-series

Chapter 12 Bagging and Random Forests

Chapter 12 Bagging and Random Forests Xiaogang Su Department of Statistics and Actuarial Science University of Central Florida - 1 - Outline A brief introduction to the bootstrap Bagging: basic concepts

Better credit models benefit us all

Better credit models benefit us all Agenda Credit Scoring - Overview Random Forest - Overview Random Forest outperform logistic regression for credit scoring out of the box Interaction term hypothesis

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century Nora Galambos, PhD Senior Data Scientist Office of Institutional Research, Planning & Effectiveness Stony Brook University AIRPO

A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier

A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier G.T. Prasanna Kumari Associate Professor, Dept of Computer Science and Engineering, Gokula Krishna College of Engg, Sullurpet-524121,

BOOSTED REGRESSION TREES: A MODERN WAY TO ENHANCE ACTUARIAL MODELLING

BOOSTED REGRESSION TREES: A MODERN WAY TO ENHANCE ACTUARIAL MODELLING Xavier Conort xavier.conort@gear-analytics.com Session Number: TBR14 Insurance has always been a data business The industry has successfully

The Operational Value of Social Media Information. Social Media and Customer Interaction

The Operational Value of Social Media Information Dennis J. Zhang (Kellogg School of Management) Ruomeng Cui (Kelley School of Business) Santiago Gallino (Tuck School of Business) Antonio Moreno-Garcia

Chapter 6. The stacking ensemble approach

82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

Classification: Basic Concepts, Decision Trees, and Model Evaluation. General Approach for Building Classification Model

10 10 Classification: Basic Concepts, Decision Trees, and Model Evaluation Dr. Hui Xiong Rutgers University Introduction to Data Mining 1//009 1 General Approach for Building Classification Model Tid Attrib1

C19 Machine Learning

C9 Machine Learning 8 Lectures Hilary Term 25 2 Tutorial Sheets A. Zisserman Overview: Supervised classification perceptron, support vector machine, loss functions, kernels, random forests, neural networks

REVIEW OF ENSEMBLE CLASSIFICATION

Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320 088X IJCSMC, Vol. 2, Issue.

Decompose Error Rate into components, some of which can be measured on unlabeled data

Bias-Variance Theory Decompose Error Rate into components, some of which can be measured on unlabeled data Bias-Variance Decomposition for Regression Bias-Variance Decomposition for Classification Bias-Variance

New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction

Introduction New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets.

Why do statisticians "hate" us?

Why do statisticians "hate" us? David Hand, Heikki Mannila, Padhraic Smyth "Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data

Machine Learning. Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos)

Machine Learning Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos) What Is Machine Learning? A computer program is said to learn from experience E with respect to some class of

Generalizing Random Forests Principles to other Methods: Random MultiNomial Logit, Random Naive Bayes, Anita Prinzie & Dirk Van den Poel

Generalizing Random Forests Principles to other Methods: Random MultiNomial Logit, Random Naive Bayes, Anita Prinzie & Dirk Van den Poel Copyright 2008 All rights reserved. Random Forests Forest of decision

Data Mining and Exploration. Data Mining and Exploration: Introduction. Relationships between courses. Overview. Course Introduction

Data Mining and Exploration Data Mining and Exploration: Introduction Amos Storkey, School of Informatics January 10, 2006 http://www.inf.ed.ac.uk/teaching/courses/dme/ Course Introduction Welcome Administration

Boosting. riedmiller@informatik.uni-freiburg.de

. Machine Learning Boosting Prof. Dr. Martin Riedmiller AG Maschinelles Lernen und Natürlichsprachliche Systeme Institut für Informatik Technische Fakultät Albert-Ludwigs-Universität Freiburg riedmiller@informatik.uni-freiburg.de

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions

Ensemble Data Mining Methods

Ensemble Data Mining Methods Nikunj C. Oza, Ph.D., NASA Ames Research Center, USA INTRODUCTION Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Unit # 10 Sajjad Haider Fall 2012 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right

Applied Multivariate Analysis - Big data analytics

Applied Multivariate Analysis - Big data analytics Nathalie Villa-Vialaneix nathalie.villa@toulouse.inra.fr http://www.nathalievilla.org M1 in Economics and Economics and Statistics Toulouse School of

MACHINE LEARNING BRETT WUJEK, SAS INSTITUTE INC.

MACHINE LEARNING BRETT WUJEK, SAS INSTITUTE INC. AGENDA MACHINE LEARNING Background Use cases in healthcare, insurance, retail and banking Eamples: Unsupervised Learning Principle Component Analysis Supervised

The Generalization Paradox of Ensembles John F. ELDER IV Ensemble models built by methods such as bagging, boosting, and Bayesian model averaging appear dauntingly complex, yet tend to strongly outperform

Advanced Ensemble Strategies for Polynomial Models

Advanced Ensemble Strategies for Polynomial Models Pavel Kordík 1, Jan Černý 2 1 Dept. of Computer Science, Faculty of Information Technology, Czech Technical University in Prague, 2 Dept. of Computer

Studying Auto Insurance Data

Studying Auto Insurance Data Ashutosh Nandeshwar February 23, 2010 1 Introduction To study auto insurance data using traditional and non-traditional tools, I downloaded a well-studied data from http://www.statsci.org/data/general/motorins.

L25: Ensemble learning

L25: Ensemble learning Introduction Methods for constructing ensembles Combination strategies Stacked generalization Mixtures of experts Bagging Boosting CSCE 666 Pattern Analysis Ricardo Gutierrez-Osuna

Government of Russian Federation. Faculty of Computer Science School of Data Analysis and Artificial Intelligence

Government of Russian Federation Federal State Autonomous Educational Institution of High Professional Education National Research University «Higher School of Economics» Faculty of Computer Science School

Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD

Predictive Analytics Techniques: What to Use For Your Big Data March 26, 2014 Fern Halper, PhD Presenter Proven Performance Since 1995 TDWI helps business and IT professionals gain insight about data warehousing,

Trees and Random Forests

Trees and Random Forests Adele Cutler Professor, Mathematics and Statistics Utah State University This research is partially supported by NIH 1R15AG037392-01 Cache Valley, Utah Utah State University Leo

Lecture 10: Regression Trees

Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,

Introduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk

Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk Ensembles 2 Learning Ensembles Learn multiple alternative definitions of a concept using different training

Introduction to Learning & Decision Trees

Artificial Intelligence: Representation and Problem Solving 5-38 April 0, 2007 Introduction to Learning & Decision Trees Learning and Decision Trees to learning What is learning? - more than just memorizing

BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376

Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.

Data Mining Practical Machine Learning Tools and Techniques. Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank Engineering the input and output Attribute selection Scheme independent, scheme

Tree Ensembles: The Power of Post- Processing. December 2012 Dan Steinberg Mikhail Golovnya Salford Systems

Tree Ensembles: The Power of Post- Processing December 2012 Dan Steinberg Mikhail Golovnya Salford Systems Course Outline Salford Systems quick overview Treenet an ensemble of boosted trees GPS modern

Using Random Forest to Learn Imbalanced Data

Using Random Forest to Learn Imbalanced Data Chao Chen, chenchao@stat.berkeley.edu Department of Statistics,UC Berkeley Andy Liaw, andy liaw@merck.com Biometrics Research,Merck Research Labs Leo Breiman,

MS1b Statistical Data Mining

MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner

Paper 3361-2015 The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner Narmada Deve Panneerselvam, Spears School of Business, Oklahoma State University, Stillwater,

Missing Data. Katyn & Elena

Missing Data Katyn & Elena What to do with Missing Data Standard is complete case analysis/listwise dele;on ie. Delete cases with missing data so only complete cases are le> Two other popular op;ons: Mul;ple

Fine Particulate Matter Concentration Level Prediction by using Tree-based Ensemble Classification Algorithms

Fine Particulate Matter Concentration Level Prediction by using Tree-based Ensemble Classification Algorithms Yin Zhao School of Mathematical Sciences Universiti Sains Malaysia (USM) Penang, Malaysia Yahya

CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.

Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

Inductive Learning in Less Than One Sequential Data Scan

Inductive Learning in Less Than One Sequential Data Scan Wei Fan, Haixun Wang, and Philip S. Yu IBM T.J.Watson Research Hawthorne, NY 10532 {weifan,haixun,psyu}@us.ibm.com Shaw-Hwa Lo Statistics Department,

When Efficient Model Averaging Out-Performs Boosting and Bagging

When Efficient Model Averaging Out-Performs Boosting and Bagging Ian Davidson 1 and Wei Fan 2 1 Department of Computer Science, University at Albany - State University of New York, Albany, NY 12222. Email:

HT2015: SC4 Statistical Data Mining and Machine Learning

HT2015: SC4 Statistical Data Mining and Machine Learning Dino Sejdinovic Department of Statistics Oxford http://www.stats.ox.ac.uk/~sejdinov/sdmml.html Bayesian Nonparametrics Parametric vs Nonparametric

Classification and Regression by randomforest

Vol. 2/3, December 02 18 Classification and Regression by randomforest Andy Liaw and Matthew Wiener Introduction Recently there has been a lot of interest in ensemble learning methods that generate many

6 Classification and Regression Trees, 7 Bagging, and Boosting

hs24 v.2004/01/03 Prn:23/02/2005; 14:41 F:hs24011.tex; VTEX/ES p. 1 1 Handbook of Statistics, Vol. 24 ISSN: 0169-7161 2005 Elsevier B.V. All rights reserved. DOI 10.1016/S0169-7161(04)24011-1 1 6 Classification

Local classification and local likelihoods

Local classification and local likelihoods November 18 k-nearest neighbors The idea of local regression can be extended to classification as well The simplest way of doing so is called nearest neighbor

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris

Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector Machines

Introduction to Bayesian Classification (A Practical Discussion) Todd Holloway Lecture for B551 Nov. 27, 2007

Introduction to Bayesian Classification (A Practical Discussion) Todd Holloway Lecture for B551 Nov. 27, 2007 Naïve Bayes Components ML vs. MAP Benefits Feature Preparation Filtering Decay Extended Examples

Introduction To Ensemble Learning

Educational Series Introduction To Ensemble Learning Dr. Oliver Steinki, CFA, FRM Ziad Mohammad July 2015 What Is Ensemble Learning? In broad terms, ensemble learning is a procedure where multiple learner

Supervised Learning (Big Data Analytics)

Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be used

COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction

COMP3420: Advanced Databases and Data Mining Classification and prediction: Introduction and Decision Tree Induction Lecture outline Classification versus prediction Classification A two step process Supervised

Cross-validation for detecting and preventing overfitting

Cross-validation for detecting and preventing overfitting Note to other teachers and users of these slides. Andrew would be delighted if ou found this source material useful in giving our own lectures.

Heritage Provider Network Health Prize Round 3 Milestone: Team crescendo s Solution

Heritage Provider Network Health Prize Round 3 Milestone: Team crescendo s Solution Rie Johnson Tong Zhang 1 Introduction This document describes our entry nominated for the second prize of the Heritage

Sales Forecasting for Retail Chains

1 Sales Forecasting for Retail Chains Ankur Jain 1, Manghat Nitish Menon 2, Saurabh Chandra 3 A53097130 1, A53097652 2, A53104614 3 {anj022 1, mnmenon 2, sbipinch 3 }@eng.ucsd.edu Abstract This paper presents

COMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Big Data by the numbers

COMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Instructor: (jpineau@cs.mcgill.ca) TAs: Pierre-Luc Bacon (pbacon@cs.mcgill.ca) Ryan Lowe (ryan.lowe@mail.mcgill.ca)

The Artificial Prediction Market

The Artificial Prediction Market Adrian Barbu Department of Statistics Florida State University Joint work with Nathan Lay, Siemens Corporate Research 1 Overview Main Contributions A mathematical theory

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models

Course Syllabus. Purposes of Course:

Course Syllabus Eco 5385.701 Predictive Analytics for Economists Summer 2014 TTh 6:00 8:50 pm and Sat. 12:00 2:50 pm First Day of Class: Tuesday, June 3 Last Day of Class: Tuesday, July 1 251 Maguire Building

Classification of Bad Accounts in Credit Card Industry

Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition

Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods

Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods Jerzy B laszczyński 1, Krzysztof Dembczyński 1, Wojciech Kot lowski 1, and Mariusz Paw lowski 2 1 Institute of Computing

Big data workloads and real-world data sets

Big data workloads and real-world data sets Gang Lu Institute of Computing Technology, Chinese Academy of Sciences BigDataBench Tutorial MICRO 2014 Cambridge, UK INSTITUTE OF COMPUTING TECHNOLOGY 1 Five

Alessandro Laio, Maria d Errico and Alex Rodriguez SISSA (Trieste)

Clustering by fast search- and- find of density peaks Alessandro Laio, Maria d Errico and Alex Rodriguez SISSA (Trieste) What is a cluster? clus ter [kluhs- ter], noun 1.a number of things of the same

II. RELATED WORK. Sentiment Mining

Sentiment Mining Using Ensemble Classification Models Matthew Whitehead and Larry Yaeger Indiana University School of Informatics 901 E. 10th St. Bloomington, IN 47408 {mewhiteh, larryy}@indiana.edu Abstract

Event driven trading new studies on innovative way. of trading in Forex market. Michał Osmoła INIME live 23 February 2016

Event driven trading new studies on innovative way of trading in Forex market Michał Osmoła INIME live 23 February 2016 Forex market From Wikipedia: The foreign exchange market (Forex, FX, or currency

THE RISE OF THE BIG DATA: WHY SHOULD STATISTICIANS EMBRACE COLLABORATIONS WITH COMPUTER SCIENTISTS XIAO CHENG. (Under the Direction of Jeongyoun Ahn)

THE RISE OF THE BIG DATA: WHY SHOULD STATISTICIANS EMBRACE COLLABORATIONS WITH COMPUTER SCIENTISTS by XIAO CHENG (Under the Direction of Jeongyoun Ahn) ABSTRACT Big Data has been the new trend in businesses.

A Feature- based Approach to Big Data Medical Image Analysis

A Feature- based Approach to Big Data Medical Image Analysis Ma\$hew Toews \$, Chris/an Wachinger, Raul San Jose Estepar, William Wells III \$ École de Technologie Supérieur, Montreal Canada BWH, Harvard

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions

SMA 50: Statistical Learning and Data Mining in Bioinformatics (also listed as 5.077: Statistical Learning and Data Mining ()) Spring Term (Feb May 200) Faculty: Professor Roy Welsch Wed 0 Feb 7:00-8:0

Data Mining Techniques for Prognosis in Pancreatic Cancer

Data Mining Techniques for Prognosis in Pancreatic Cancer by Stuart Floyd A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUE In partial fulfillment of the requirements for the Degree