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

Save this PDF as:

Size: px
Start display at page:

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

## Transcription

1 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 Attrib Attrib3 Class 1 Yes Large 15K No No Medium 100K No 3 No Small 70K No 4 Yes Medium 10K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 0K No 8 No Small 85K Yes 9 No Medium 75K No Learn Model 10 No Small 90K Yes Tid Attrib1 Attrib Attrib3 Class Apply Model 11 No Small 55K? 1 Yes Medium 80K? 13 Yes Large 110K? 14 No Small 95K? 15 No Large 67K? Introduction to Data Mining 1//009

2 Classification Techniques Base Classifiers Decision Tree based Methods Rule-based Methods Nearest-neighbor Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines Ensemble Classifiers Boosting, Bagging, Random Forests Introduction to Data Mining 1//009 3 Classification: Model Overfitting and Classifier Evaluation Dr. Hui Xiong Rutgers University Introduction to Data Mining 1//009 4

3 Classification Errors Training errors (apparent errors) Errors committed on the training set Test errors Errors committed on the test set Generalization errors Expected error of a model over random selection of records from same distribution Introduction to Data Mining 1//009 5 Example Data Set Two class problem: +, o 3000 data points (30% for training, 70% for testing) Data set for + class is generated from a uniform distribution Data set for o class is generated from a mixture of 3 gaussian distributions, centered at (5,15), (10,5), and (15,15) Introduction to Data Mining 1//009 6

4 Decision Trees Decision Tree with 11 leaf nodes Decision Tree with 4 leaf nodes Which tree is better? Introduction to Data Mining 1//009 7 Model Overfitting Underfitting: when model is too simple, both training and test errors are large Overfitting: when model is too complex, training error is small but test error is large Introduction to Data Mining 1//009 8

5 Mammal Classification Problem Training Set Decision Tree Model training error = 0% Introduction to Data Mining 1//009 9 Effect of Noise Example: Mammal Classification problem Training Set: Model M1: train err = 0%, test err = 30% Test Set: Model M: train err = 0%, test err = 10% Introduction to Data Mining 1//009 10

6 Lack of Representative Samples Training Set: Test Set: Model M3: train err = 0%, test err = 30% Lack of training records at the leaf nodes for making reliable classification Introduction to Data Mining 1// Effect of Multiple Comparison Procedure Consider the task of predicting whether stock market will rise/fall in the next 10 trading days Random guessing: P(correct) = 0.5 Make 10 random guesses in a row: P(# correct 8) = = Day 1 Day Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 Up Down Down Up Down Down Up Up Up Down Introduction to Data Mining 1//009 1

7 Effect of Multiple Comparison Procedure Approach: Get 50 analysts Each analyst makes 10 random guesses Choose the analyst that makes the most number of correct predictions Probability that at least one analyst makes at least 8 correct predictions P(# correct 8) = 1 ( ) 50 = Introduction to Data Mining 1// Effect of Multiple Comparison Procedure Many algorithms employ the following greedy strategy: Initial model: M Alternative model: M = M γ, where γ is a component to be added to the model (e.g., a test condition of a decision tree) Keep M if improvement, Δ(M,M ) > α Often times, γ is chosen from a set of alternative components, Γ = {γ 1, γ,, γ k } If many alternatives are available, one may inadvertently add irrelevant components to the model, resulting in model overfitting Introduction to Data Mining 1//009 14

8 Notes on Overfitting Overfitting results in decision trees that are more complex than necessary Training error no longer provides a good estimate of how well the tree will perform on previously unseen records Need new ways for estimating generalization errors Introduction to Data Mining 1// Estimating Generalization Errors Resubstitution Estimate Incorporating Model Complexity Estimating Statistical Bounds Use Validation Set Introduction to Data Mining 1//009 16

9 Resubstitution Estimate Using training error as an optimistic estimate of generalization error e(t L ) = 4/4 e(t R ) = 6/4 Introduction to Data Mining 1// Incorporating Model Complexity Rationale: Occam s Razor Given two models of similar generalization errors, one should prefer the simpler model over the more complex model A complex model has a greater chance of being fitted accidentally by errors in data Therefore, one should include model complexity when evaluating a model Introduction to Data Mining 1//009 18

10 Pessimistic Estimate Given a decision tree node t n(t): number of training records classified by t e(t): misclassification error of node t Training error of tree T: e' ( T ) = [ e( ti ) + Ω( ti )] i n( ti ) Ω: is the cost of adding a node N: total number of training records i e( T ) + Ω( T ) = N Introduction to Data Mining 1// Pessimistic Estimate e(t L ) = 4/4 e(t R ) = 6/4 Ω = 1 e (T L ) = (4 +7 1)/4 = e (T R ) = ( )/4 = Introduction to Data Mining 1//009 0

11 Minimum Description Length (MDL) X y X 1 1 X 0 X 3 0 X 4 1 X n 1 A Yes 0 A? No B? B 1 B C? 1 C 1 C 0 1 B X y X 1? X? X 3? X 4? X n? Cost(Model,Data) = Cost(Data Model) + Cost(Model) Cost is the number of bits needed for encoding. Search for the least costly model. Cost(Data Model) encodes the misclassification errors. Cost(Model) uses node encoding (number of children) plus splitting condition encoding. Introduction to Data Mining 1//009 1 Using Validation Set Divide training data into two parts: Training set: use for model building Validation set: use for estimating generalization error Note: validation set is not the same as test set Drawback: Less data available for training Introduction to Data Mining 1//009

12 Handling Overfitting in Decision Tree Pre-Pruning (Early Stopping Rule) Stop the algorithm before it becomes a fully-grown tree Typical stopping conditions for a node: Stop if all instances belong to the same class Stop if all the attribute values are the same More restrictive conditions: Stop if number of instances is less than some user-specified threshold Stop if class distribution of instances are independent of the available features (e.g., using χ test) Stop if expanding the current node does not improve impurity measures (e.g., Gini or information gain). Stop if estimated generalization error falls below certain threshold Introduction to Data Mining 1//009 3 Handling Overfitting in Decision Tree Post-pruning Grow decision tree to its entirety Subtree replacement Trim the nodes of the decision tree in a bottom-up fashion If generalization error improves after trimming, replace sub-tree by a leaf node Class label of leaf node is determined from majority class of instances in the sub-tree Subtree raising Replace subtree with most frequently used branch Introduction to Data Mining 1//009 4

13 Example of Post-Pruning Class = Yes 0 Class = No 10 Error = 10/30 A? Training Error (Before splitting) = 10/30 Pessimistic error = ( )/30 = 10.5/30 Training Error (After splitting) = 9/30 Pessimistic error (After splitting) = ( )/30 = 11/30 PRUNE! A1 A A3 A4 Class = Yes 8 Class = Yes 3 Class = Yes 4 Class = Yes 5 Class = No 4 Class = No 4 Class = No 1 Class = No 1 Introduction to Data Mining 1//009 5 Examples of Post-pruning Introduction to Data Mining 1//009 6

14 Evaluating Performance of Classifier Model Selection Performed during model building Purpose is to ensure that model is not overly complex (to avoid overfitting) Need to estimate generalization error Model Evaluation Performed after model has been constructed Purpose is to estimate performance of classifier on previously unseen data (e.g., test set) Introduction to Data Mining 1//009 7 Methods for Classifier Evaluation Holdout Reserve k% for training and (100-k)% for testing Random subsampling Repeated holdout Cross validation Partition data into k disjoint subsets k-fold: train on k-1 partitions, test on the remaining one Leave-one-out: k=n Bootstrap Sampling with replacement.63 bootstrap: 1 accboot = b ( 0.63 acci accs ) Introduction to Data Mining 1//009 8 b i= 1

15 Methods for Comparing Classifiers Given two models: Model M1: accuracy = 85%, tested on 30 instances Model M: accuracy = 75%, tested on 5000 instances Can we say M1 is better than M? How much confidence can we place on accuracy of M1 and M? Can the difference in performance measure be explained as a result of random fluctuations in the test set? Introduction to Data Mining 1//009 9 Confidence Interval for Accuracy Prediction can be regarded as a Bernoulli trial A Bernoulli trial has possible outcomes Coin toss head/tail Prediction correct/wrong Collection of Bernoulli trials has a Binomial distribution: x Bin(N, p) Estimate number of events x: number of correct predictions Given N and p, find P(x=k) or E(x) Example: Toss a fair coin 50 times, how many heads would turn up? Expected number of heads = N p = = 5 Introduction to Data Mining 1//009 30

16 Confidence Interval for Accuracy Estimate parameter of distribution Given x (# of correct predictions) or equivalently, acc=x/n, and N (# of test instances), Find upper and lower bounds of p (true accuracy of model) Introduction to Data Mining 1// Confidence Interval for Accuracy Area = 1 - α For large test sets (N > 30), acc has a normal distribution with mean p and variance p(1-p)/n ( acc p P Z < < Z ) α / 1 α / p(1 p) / N = 1 α Confidence Interval for p: N acc + Z p = α / ± Z α/ Z 1- α / Z + 4 N acc 4 N acc α / ( N + Z ) Introduction to Data Mining 1//009 3 α /

17 Confidence Interval for Accuracy Consider a model that produces an accuracy of 80% when evaluated on 100 test instances: N=100, acc = α Z Let 1-α = 0.95 (95% confidence) From probability table, Z α/ = N p(lower) p(upper) Introduction to Data Mining 1// Comparing Performance of Models Given two models, say M1 and M, which is better? M1 is tested on D1 (size=n1), found error rate = e 1 M is tested on D (size=n), found error rate = e Assume D1 and D are independent If n1 and n are sufficiently large, then e e ( μ 1 1 ) ( μ, ) 1 N, ~ σ ~ N σ Approximate: e (1 e ) i i ˆ σ = i n Introduction to Data Mining 1// i

18 Comparing Performance of Models To test if performance difference is statistically significant: d = e1 e d ~ N(d t,σ t ) where d t is the true difference Since D1 and D are independent, their variance adds up: σ = σ + σ ˆ σ + ˆ σ t 1 e1(1 e1) e(1 e) = + n1 n 1 At (1-α) confidence level, d t = d ± Z α / ˆ σ t Introduction to Data Mining 1// An Illustrative Example Given: M1: n1 = 30, e1 = 0.15 M: n = 5000, e = 0.5 d = e e1 = (-sided test) 0.15(1 0.15) 0.5(1 0.5) ˆ = + = σ d At 95% confidence level, Z α/ =1.96 d t = ± = ± 0.18 => Interval contains 0 => difference may not be statistically significant Introduction to Data Mining 1//009 36

19 Support Vector Machines (SVMs) SVMs are a rare example of a methodology where geometric intuition, elegant mathematics, theoretical guarantees, and practical use meet. Find a linear hyperplane (decision boundary) that separates the data Introduction to Data Mining 1// Support Vector Machines One Possible Solution Introduction to Data Mining 1//009 38

20 Support Vector Machines Another possible solution Introduction to Data Mining 1// Support Vector Machines Other possible solutions Introduction to Data Mining 1//009 40

21 Support Vector Machines Which one is better? B1 or B? How do you define better? Introduction to Data Mining 1// Support Vector Machines Find hyperplane maximizes the margin => B1 is better than B Introduction to Data Mining 1//009 4

22 Support Vector Machines w r x r + b = 0 w r x r + b = 1 w r x r + b = + 1 r r r 1 if w x + b 1 ( x ) = r r 1 if w x + b 1 Margin = w r f Introduction to Data Mining 1// Support Vector Machines We want to maximize: Margin = w r r w Which h is equivalent to minimizing: i i i L (w) w = But subjected to the following constraints: r r r 1 if w x i + b 1 f ( x i ) = r r 1 if w x i + b 1 This is a constrained optimization problem Numerical approaches to solve it (e.g., quadratic programming) Introduction to Data Mining 1//009 44

23 Support Vector Machines What if the problem is not linearly separable? Introduction to Data Mining 1// Support Vector Machines What if the problem is not linearly separable? Introduce slack variables r N w k L( w) = + C ξ i i= 1 Need to minimize: Subject to: r f ( x i r r 1 if w x i + b 1 - ξ i ) = r r 1 if w x i + b 1 + ξ i Introduction to Data Mining 1//009 46

24 Nonlinear Support Vector Machines What if decision boundary is not linear? x X x 1 X X X O O O O X X x 1 X O O O O X x 1 Introduction to Data Mining 1// Mapping into a New Feature Space Φ : x X = Φ(x) Φ(x 1,x ) = (x 1,x,x 1,x,x 1 x ) Rather than run SVM on x i, run it on Φ(x i ) Find non-linear separator in input space What if Φ(x i ) is really big? Use kernels! Introduction to Data Mining 1//009 48

25 Examples of Kernel Functions Polynomial kernel with degree d Radial basis function kernel with width σ Sigmoid with parameter κ and θ It does not satisfy the Mercer condition on all κ and θ Introduction to Data Mining 1// Characteristics of SVMs Perform best on the average or outperform other techniques across many important applications The results are stable, reproducible, and largely independent of the specific optimization algorithm A convex optimization problem Lead to the global optimum The parameter selection problem The type of kernels (including its parameters) The attributes t to be included. d The results are hard to interpret Computational challenge Typically quadratic and multi-scan of the data Introduction to Data Mining 1//009 50

### Lecture Notes for Chapter 4. Introduction to Data Mining

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data

### Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data

### Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data

### Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2. Tid Refund Marital Status

Data Mining Classification: Basic Concepts, Decision Trees, and Evaluation Lecture tes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Classification: Definition Given a collection of

### Data Mining Classification: Decision Trees

Data Mining Classification: Decision Trees Classification Decision Trees: what they are and how they work Hunt s (TDIDT) algorithm How to select the best split How to handle Inconsistent data Continuous

### 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

### Data Mining for Knowledge Management. Classification

1 Data Mining for Knowledge Management Classification Themis Palpanas University of Trento http://disi.unitn.eu/~themis Data Mining for Knowledge Management 1 Thanks for slides to: Jiawei Han Eamonn Keogh

### Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal

Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether

### 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

### 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

### 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

### Introduction to Support Vector Machines. Colin Campbell, Bristol University

Introduction to Support Vector Machines Colin Campbell, Bristol University 1 Outline of talk. Part 1. An Introduction to SVMs 1.1. SVMs for binary classification. 1.2. Soft margins and multi-class classification.

### Support Vector Machines Explained

March 1, 2009 Support Vector Machines Explained Tristan Fletcher www.cs.ucl.ac.uk/staff/t.fletcher/ Introduction This document has been written in an attempt to make the Support Vector Machines (SVM),

### A Simple Introduction to Support Vector Machines

A Simple Introduction to Support Vector Machines Martin Law Lecture for CSE 802 Department of Computer Science and Engineering Michigan State University Outline A brief history of SVM Large-margin linear

### Support Vector Machine (SVM)

Support Vector Machine (SVM) CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Margin concept Hard-Margin SVM Soft-Margin SVM Dual Problems of Hard-Margin

### 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

### 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

### Support Vector Machines with Clustering for Training with Very Large Datasets

Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano

### Decision-Tree Learning

Decision-Tree Learning Introduction ID3 Attribute selection Entropy, Information, Information Gain Gain Ratio C4.5 Decision Trees TDIDT: Top-Down Induction of Decision Trees Numeric Values Missing Values

### Classification and Prediction

Classification and Prediction Slides for Data Mining: Concepts and Techniques Chapter 7 Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser

### CS570 Introduction to Data Mining. Classification and Prediction. Partial slide credits: Han and Kamber Tan,Steinbach, Kumar

CS570 Introduction to Data Mining Classification and Prediction Partial slide credits: Han and Kamber Tan,Steinbach, Kumar 1 Classification and Prediction Overview Classification algorithms and methods

### 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

### 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

### Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

### L13: cross-validation

Resampling methods Cross validation Bootstrap L13: cross-validation Bias and variance estimation with the Bootstrap Three-way data partitioning CSCE 666 Pattern Analysis Ricardo Gutierrez-Osuna CSE@TAMU

### 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

### 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

### CLASSIFICATION AND CLUSTERING. Anveshi Charuvaka

CLASSIFICATION AND CLUSTERING Anveshi Charuvaka Learning from Data Classification Regression Clustering Anomaly Detection Contrast Set Mining Classification: Definition Given a collection of records (training

### Data mining techniques: decision trees

Data mining techniques: decision trees 1/39 Agenda Rule systems Building rule systems vs rule systems Quick reference 2/39 1 Agenda Rule systems Building rule systems vs rule systems Quick reference 3/39

### 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

### Classifying Large Data Sets Using SVMs with Hierarchical Clusters. Presented by :Limou Wang

Classifying Large Data Sets Using SVMs with Hierarchical Clusters Presented by :Limou Wang Overview SVM Overview Motivation Hierarchical micro-clustering algorithm Clustering-Based SVM (CB-SVM) Experimental

### 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,

### 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

### Classification and Prediction

Classification and Prediction 1. Objectives...2 2. Classification vs. Prediction...3 2.1. Definitions...3 2.2. Supervised vs. Unsupervised Learning...3 2.3. Classification and Prediction Related Issues...4

### Evaluation and Credibility. How much should we believe in what was learned?

Evaluation and Credibility How much should we believe in what was learned? Outline Introduction Classification with Train, Test, and Validation sets Handling Unbalanced Data; Parameter Tuning Cross-validation

### Introduction to Machine Learning. Rob Schapire Princeton University. schapire

Introduction to Machine Learning Rob Schapire Princeton University www.cs.princeton.edu/ schapire Machine Learning studies how to automatically learn to make accurate predictions based on past observations

### 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

### Decision Support Systems

Decision Support Systems 50 (2011) 602 613 Contents lists available at ScienceDirect Decision Support Systems journal homepage: www.elsevier.com/locate/dss Data mining for credit card fraud: A comparative

### Lecture 13: Validation

Lecture 3: Validation g Motivation g The Holdout g Re-sampling techniques g Three-way data splits Motivation g Validation techniques are motivated by two fundamental problems in pattern recognition: model

### Neural Networks and Support Vector Machines

INF5390 - Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF5390-13 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines

### Introduction to Hypothesis Testing. Point estimation and confidence intervals are useful statistical inference procedures.

Introduction to Hypothesis Testing Point estimation and confidence intervals are useful statistical inference procedures. Another type of inference is used frequently used concerns tests of hypotheses.

### Classifiers & Classification

Classifiers & Classification Forsyth & Ponce Computer Vision A Modern Approach chapter 22 Pattern Classification Duda, Hart and Stork School of Computer Science & Statistics Trinity College Dublin Dublin

### Detecting Corporate Fraud: An Application of Machine Learning

Detecting Corporate Fraud: An Application of Machine Learning Ophir Gottlieb, Curt Salisbury, Howard Shek, Vishal Vaidyanathan December 15, 2006 ABSTRACT This paper explores the application of several

### Support Vector Machines for Classification and Regression

UNIVERSITY OF SOUTHAMPTON Support Vector Machines for Classification and Regression by Steve R. Gunn Technical Report Faculty of Engineering, Science and Mathematics School of Electronics and Computer

### 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:

### Acknowledgments. Data Mining with Regression. Data Mining Context. Overview. Colleagues

Data Mining with Regression Teaching an old dog some new tricks Acknowledgments Colleagues Dean Foster in Statistics Lyle Ungar in Computer Science Bob Stine Department of Statistics The School of the

### Neural Networks. CAP5610 Machine Learning Instructor: Guo-Jun Qi

Neural Networks CAP5610 Machine Learning Instructor: Guo-Jun Qi Recap: linear classifier Logistic regression Maximizing the posterior distribution of class Y conditional on the input vector X Support vector

### Semi-Supervised Support Vector Machines and Application to Spam Filtering

Semi-Supervised Support Vector Machines and Application to Spam Filtering Alexander Zien Empirical Inference Department, Bernhard Schölkopf Max Planck Institute for Biological Cybernetics ECML 2006 Discovery

### Making Sense of the Mayhem: Machine Learning and March Madness

Making Sense of the Mayhem: Machine Learning and March Madness Alex Tran and Adam Ginzberg Stanford University atran3@stanford.edu ginzberg@stanford.edu I. Introduction III. Model The goal of our research

### CS 2750 Machine Learning. Lecture 1. Machine Learning. CS 2750 Machine Learning.

Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x-5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

### Increasing Classification Accuracy. Data Mining: Bagging and Boosting. Bagging 1. Bagging 2. Bagging. Boosting Meta-learning (stacking)

Data Mining: Bagging and Boosting Increasing Classification Accuracy Andrew Kusiak 2139 Seamans Center Iowa City, Iowa 52242-1527 andrew-kusiak@uiowa.edu http://www.icaen.uiowa.edu/~ankusiak Tel: 319-335

### Learning. Artificial Intelligence. Learning. Types of Learning. Inductive Learning Method. Inductive Learning. Learning.

Learning Learning is essential for unknown environments, i.e., when designer lacks omniscience Artificial Intelligence Learning Chapter 8 Learning is useful as a system construction method, i.e., expose

### Machine Learning Algorithms for Classification. Rob Schapire Princeton University

Machine Learning Algorithms for Classification Rob Schapire Princeton University Machine Learning studies how to automatically learn to make accurate predictions based on past observations classification

### !"!!"#\$\$%&'()*+\$(,%!"#\$%\$&'()*""%(+,'-*&./#-\$&'(-&(0*".\$#-\$1"(2&."3\$'45"

!"!!"#\$\$%&'()*+\$(,%!"#\$%\$&'()*""%(+,'-*&./#-\$&'(-&(0*".\$#-\$1"(2&."3\$'45"!"#"\$%&#'()*+',\$\$-.&#',/"-0%.12'32./4'5,5'6/%&)\$).2&'7./&)8'5,5'9/2%.%3%&8':")08';:

### 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

### Data Mining Practical Machine Learning Tools and Techniques

Credibility: Evaluating what s been learned Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 5 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Issues: training, testing,

### A Study on the Comparison of Electricity Forecasting Models: Korea and China

Communications for Statistical Applications and Methods 2015, Vol. 22, No. 6, 675 683 DOI: http://dx.doi.org/10.5351/csam.2015.22.6.675 Print ISSN 2287-7843 / Online ISSN 2383-4757 A Study on the Comparison

### 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

### STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

### 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

### Early defect identification of semiconductor processes using machine learning

STANFORD UNIVERISTY MACHINE LEARNING CS229 Early defect identification of semiconductor processes using machine learning Friday, December 16, 2011 Authors: Saul ROSA Anton VLADIMIROV Professor: Dr. Andrew

### Data Mining Techniques Chapter 6: Decision Trees

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

### Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu

Introduction to Machine Learning Lecture 1 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction Logistics Prerequisites: basics concepts needed in probability and statistics

### 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

### Beating the NCAA Football Point Spread

Beating the NCAA Football Point Spread Brian Liu Mathematical & Computational Sciences Stanford University Patrick Lai Computer Science Department Stanford University December 10, 2010 1 Introduction Over

### Content-Based Recommendation

Content-Based Recommendation Content-based? Item descriptions to identify items that are of particular interest to the user Example Example Comparing with Noncontent based Items User-based CF Searches

### 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

### 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.

### Evaluation & Validation: Credibility: Evaluating what has been learned

Evaluation & Validation: Credibility: Evaluating what has been learned How predictive is a learned model? How can we evaluate a model Test the model Statistical tests Considerations in evaluating a Model

### Introduction to Machine Learning NPFL 054

Introduction to Machine Learning NPFL 054 http://ufal.mff.cuni.cz/course/npfl054 Barbora Hladká hladka@ufal.mff.cuni.cz Martin Holub holub@ufal.mff.cuni.cz Charles University, Faculty of Mathematics and

### Analysis of kiva.com Microlending Service! Hoda Eydgahi Julia Ma Andy Bardagjy December 9, 2010 MAS.622j

Analysis of kiva.com Microlending Service! Hoda Eydgahi Julia Ma Andy Bardagjy December 9, 2010 MAS.622j What is Kiva? An organization that allows people to lend small amounts of money via the Internet

### Reference Books. Data Mining. Supervised vs. Unsupervised Learning. Classification: Definition. Classification k-nearest neighbors

Classification k-nearest neighbors Data Mining Dr. Engin YILDIZTEPE Reference Books Han, J., Kamber, M., Pei, J., (2011). Data Mining: Concepts and Techniques. Third edition. San Francisco: Morgan Kaufmann

### 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

### Notes on Support Vector Machines

Notes on Support Vector Machines Fernando Mira da Silva Fernando.Silva@inesc.pt Neural Network Group I N E S C November 1998 Abstract This report describes an empirical study of Support Vector Machines

### Machine Learning and Pattern Recognition Logistic Regression

Machine Learning and Pattern Recognition Logistic Regression Course Lecturer:Amos J Storkey Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Crichton Street,

### 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

### Data mining knowledge representation

Data mining knowledge representation 1 What Defines a Data Mining Task? Task relevant data: where and how to retrieve the data to be used for mining Background knowledge: Concept hierarchies Interestingness

### Professor Anita Wasilewska. Classification Lecture Notes

Professor Anita Wasilewska Classification Lecture Notes Classification (Data Mining Book Chapters 5 and 7) PART ONE: Supervised learning and Classification Data format: training and test data Concept,

### Chapter 4 Data Mining A Short Introduction

Chapter 4 Data Mining A Short Introduction Data Mining - 1 1 Today's Question 1. Data Mining Overview 2. Association Rule Mining 3. Clustering 4. Classification Data Mining - 2 2 3. Clustering - Descriptive

### 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

### An Introduction to Machine Learning

An Introduction to Machine Learning L5: Novelty Detection and Regression Alexander J. Smola Statistical Machine Learning Program Canberra, ACT 0200 Australia Alex.Smola@nicta.com.au Tata Institute, Pune,

### Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence

Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support

### 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

### Chapter 4: Non-Parametric Classification

Chapter 4: Non-Parametric Classification Introduction Density Estimation Parzen Windows Kn-Nearest Neighbor Density Estimation K-Nearest Neighbor (KNN) Decision Rule Gaussian Mixture Model A weighted combination

### Data Mining Part 5. Prediction

Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

### 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

### Classification algorithm in Data mining: An Overview

Classification algorithm in Data mining: An Overview S.Neelamegam #1, Dr.E.Ramaraj *2 #1 M.phil Scholar, Department of Computer Science and Engineering, Alagappa University, Karaikudi. *2 Professor, Department

### 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 &

### A SURVEY OF TEXT CLASSIFICATION ALGORITHMS

Chapter 6 A SURVEY OF TEXT CLASSIFICATION ALGORITHMS Charu C. Aggarwal IBM T. J. Watson Research Center Yorktown Heights, NY charu@us.ibm.com ChengXiang Zhai University of Illinois at Urbana-Champaign

### Homework Assignment 7

Homework Assignment 7 36-350, Data Mining Solutions 1. Base rates (10 points) (a) What fraction of the e-mails are actually spam? Answer: 39%. > sum(spam\$spam=="spam") [1] 1813 > 1813/nrow(spam) [1] 0.3940448

### Data Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University

Data Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Models vs. Patterns Models A model is a high level, global description of a

### Support Vector Machines

Support Vector Machines Here we approach the two-class classification problem in a direct way: We try and find a plane that separates the classes in feature space. If we cannot, we get creative in two

### Improving Generalization

Improving Generalization Introduction to Neural Networks : Lecture 10 John A. Bullinaria, 2004 1. Improving Generalization 2. Training, Validation and Testing Data Sets 3. Cross-Validation 4. Weight Restriction

### 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:

### 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

### Model Trees for Classification of Hybrid Data Types

Model Trees for Classification of Hybrid Data Types Hsing-Kuo Pao, Shou-Chih Chang, and Yuh-Jye Lee Dept. of Computer Science & Information Engineering, National Taiwan University of Science & Technology,

### Data Mining with R. Decision Trees and Random Forests. Hugh Murrell

Data Mining with R Decision Trees and Random Forests Hugh Murrell reference books These slides are based on a book by Graham Williams: Data Mining with Rattle and R, The Art of Excavating Data for Knowledge

### AUTOMATION OF ENERGY DEMAND FORECASTING. Sanzad Siddique, B.S.

AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty of the Graduate School, Marquette University, in Partial Fulfillment of the Requirements for the Degree