# COMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection.

Save this PDF as:

Size: px
Start display at page:

Download "COMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection."

## Transcription

1 COMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection. Instructor: Class web page: Unless otherwise noted, all material posted for this course are copyright of the instructor, and cannot be reused or reposted without the i nstructor s written permission. Evaluating performance Different objectives: Selecting the right model for a problem. Testing performance of a new algorithm. Evaluating impact on a new application. 2 1

2 The experimental design approach In science, we often want to test a hypothesis of the form variable X influences behavior of variable Y. X might be the type of learning algorithm and Y may be the classification accuracy. X might be the number of degrees of freedom in the polynomial regression function and Y may be the mean-squared error. We could tackle this by running an experiment where we vary X and measure Y. Is this sufficient? 3 Overfitting Adding more degrees of freedom (more features) always seems to improve the solution! 4 2

3 Train vs test error Train error is the average loss on the dataset used to train the algorithm. Usually improves with increased model complexity. Test error (or generalization error) is the average loss measured on a hold-out dataset. Usually improves with more training data. Prediction Error High Bias Low Variance Low Bias High Variance Both are measured for a given loss function, e.g. Err(x) = i=1:n ( y i - w T x i ) Model Complexity (df) FIGURE 7.1. Behavior of test sample and training sample error as the model complexity is varied. The light blue curves show the training error err, while the light red curves show the conditional test error ErrT for 100 training sets of size 50 each, as the model complexity is increased. The solid curves show the expected test error Err and the expected training error E[err]. 5 Minimizing the error Find the low point in the testing error: Prediction Error High Bias Low Variance Low Bias High Variance Test error Train error Model Complexity (df) 6 3

4 Understanding the error Consider the probabilistic version of linear regression: Y = h w (X) + ε, where ε ~ N(0,σ 2 ). Then the error: Err(x) = E[ (Y-h w (x)) 2 X=x] = σ 2 + [Eĥ(x) h(x)] 2 + E[ĥ(x)-E ĥ(x)] 2 = σ 2 + Bias 2 + Variance The first term contains the irreducible error (variance of the target around its true mean). The other two terms can usually be traded-off. High bias, low var. (simple model) Low bias, high var. (complex model) 7 Estimating the test error Single test-train split: Estimation test error with high variance. 4-fold test-train splits: Better estimation of the test error, because it is averaged over four different test-train splits. 8 4

5 K-fold cross-validation K=1: High variance estimate of Err(). Fast to compute. K>1: Improved estimate of Err(); wastes 1/K of the data. K times more expensive to compute. K=N: Lowest variance estimate of Err(). Doesn t waste data. N times slower to compute than for single test/train split. 9 Real-world classification tasks

6 Strategy #1 Consider a classification problem with a large number of features, greater than the number of examples (m>>n). Consider the following strategies to avoid over-fitting in such a problem. Strategy 1: 1. Check for correlation between each feature (individually) and the output. Keep a small set of features showing strong correlation. 2. Divide the examples into k groups at random. 3. Using the features from step 1 and the examples from k-1 groups from step 2, build a classifier. 4. Use this classifier to predict the output for the examples in group k and measure the error. 5. Repeat steps 3-4 for each group to produce the cross-validation estimate of the error. 11 Strategy #2 Consider a classification problem with a large number of features, greater than the number of examples (m>>n). Consider the following strategies to avoid over-fitting in such a problem. Strategy 2: 1. Divide the examples into k groups at random. 2. For each group, find a small set of features showing strong correlation with the output. 3. Using the features and examples from k-1 groups from step 1, build a classifier. 4. Use this classifier to predict the output for the examples in group k and measure the error. 5. Repeat 2-4 for each group to produce the cross-validation estimate of the error. 12 6

7 Strategy #3 Consider a classification problem with a large number of features, greater than the number of examples (m>>n). Consider the following strategies to avoid over-fitting in such a problem. Strategy 3: 1. Randomly sample n examples. 2. For the sampled data, find a small set of features showing strong correlation with the outptut 3. Using the examples from step 1 and features from step 2, build a classifier. 4. Use this classifier to predict the output for those examples in the dataset that are not in n and measure the error. 5. Repeat steps 1-4 k times to produce the cross-validation estimate of the error. 13 Summary of 3 strategies Strategy 1: 1. Check for correlation between each feature (individually) and the output. Keep a small set of features showing strong correlation. 2. Divide the examples into k groups at random. 3. Using the features from step 1 and the examples from k-1 groups from step 2, build a classifier. 4. Use this classifier to predict the output for the examples in group k and measure the error. 5. Repeat steps 3-4 for each group to produce the cross-validation estimate of the error. Strategy 2: 1. Divide the examples into k groups at random. 2. For each group, find a small set of features showing strong correlation with the output. 3. Using the features and examples from k-1 groups from step 1, build a classifier. 4. Use this classifier to predict the output for the examples in group k and measure the error. 5. Repeat 2-4 for each group to produce the cross-validation estimate of the error. Strategy 3: 1. Randomly sample n examples. 2. For the sampled data, find a small set of features showing strong correlation with the ouptut 3. Using the examples from step 1 and features from step 2, build a classifier. 4. Use this classifier to predict the output for those examples in the dataset that are not in n and measure the error. 5. Repeat steps 1-4 k times to produce the cross-validation estimate of the error. 14 7

8 Discussion Strategy 1 is prone to overfitting, because the full dataset is considered in step 1, to select the features. Thus we do not get an unbiased estimate of the generalization error in step 5. Strategy 2 is closest to standard k-fold cross-validation. One can view the joint procedure of selecting the features and building the classifier as the training step, to be applied (separately) on each training fold. Strategy 3 is closer to a bootstrap estimate. It can give a good estimate of the generalization error, but the estimate will possibly have higher variance than the one obtained using Strategy A word of caution Intensive use of cross-validation can overfit! E.g. Given a dataset with 50 examples and 1000 features. Consider 1000 linear regression models, each built with a single feature. The best of those 1000 will look very good! But it would have looked good even if the output was random! What should we do about this? 16 8

9 A solution When you need to optimize many parameters of your model or learning algorithm. Use three datasets: The training set is used to estimate the parameters of the model. The validation set is used to estimate the prediction error for the given model. The test set is used to estimate the generalization error once the model is fixed. Train Validation Test 17 Kaggle

10 Brief aside: Bootstrapping Basic idea: Given a dataset D with N examples. Randomly draw (with replacement) B datasets of size N from D. Estimate the measure of interest on each of the B datasets. Take the mean of the estimates. Err 1 Err 2 Err B Is this a good measure for estimating the error? D 1 D 2 D B D True data distribution 19 Bootstrapping the error Use a dataset b to fit a hypothesis f b. Use the original dataset D to evaluate the error. Average over all bootstrap sets b in B. Êrr boot = 1 1 B N L(y i, B N ˆf b (x i )). b=1 i=1 Problem: Some of the same samples are used for training the learning and testing. Better idea: Include the error of a data sample i only over classifiers trained with those bootstrap sets b in which i isn t included (denoted C -i ). Êrr (1) = 1 N 1 N C i L(y i, ˆf b (x i )). i=1 b C i (Note: Bootstrapping is a very general ideal, which can be applied for empirically estimating many different quantities.) 20 10

11 Performance metrics for classification We now know how to estimate / minimize error. But there are different types of mistakes, particularly in the classification setting. E.g. Consider the diagnostic of a disease. Two types of mis-diagnostics: Patient does not have disease but received positive diagnostic (Type I error); Patient has disease but it was not detected (Type II error). E.g. Consider the problem of spam classification: A message that is not spam is assigned to the spam folder (Type I error); A message that is spam appears in the regular folder (Type II error). How many Type I errors are you willing to tolerate, for a reasonable rate of Type II errors? 21 Performance metrics for classification Examples: What do you understand from all this? 22 11

12 Example 1 23 Example

13 Example 3 25 Terminology Type of classification outputs: True positive (m11): Example of class 1 predicted as class 1. False positive (m01): Example of class 0 predicted as class 1. Type 1 error. True negative (m00): Example of class 0 predicted as class 0. False negative (m10): Example of class 1 predicted as class 0. Type II error. Total number of instances: m = m00 + m01 + m10 + m11 Error rate: (m01 + m10) / m If the classes are imbalanced (e.g. 10% from class 1, 90% from class 0), one can achieve low error (e.g. 10%) by classifying everything as coming from class 0! 26 13

14 Confusion matrix Many software packages output this matrix. apple m00 m 01 m 10 m 11 Be careful! Sometimes the format is slightly different (E.g Common measures Accuracy = (TP+ TN) / (TP + FP + FN + TN) Precision = True positives / Total number of declared positives Tex t = TP / (TP+ FP) classification Recall = True positives / Total number of actual positives = TP / (TP + FN) Sensitivity is the same as recall. Medicine Specificity = True negatives / Total number of actual negatives = TN / (FP + TN) False positive rate = FP / (FP + TN) F1 measure 28 14

15 Trade-off Often have a trade-off between false positives and false negatives. E.g. Consider 30 different classifiers trained on a class. Classify a new sample as positive if K classifiers output positive. Vary K between 0 and Receiver-operator characteristic (ROC) curve Characterizes the performance of a binary classifier over a range of classification thresholds Data from 4 prediction results: ROC curve: Example from:

16 Understanding the ROC curve Consider a classification problem where data is generated by 2 Gaussians (blue = negative class; red = positive class). Consider the decision boundary (shown as a vertical line on the left figure), where you predict Negative on the left of the boundary and predict Positive on the right of the boundary. Changing that boundary defines the ROC curve on the right. Predict negative Predictive positive Figures from: 31 Building the ROC curve In many domains, the empirical ROC curve will be non-convex (red line). Take the convex hull of the points (blue line)

17 Using the ROC curve To compare 2 algorithms over a range of classification thresholds, consider the Area Under the Curve (AUC). A perfect algorithm has AUC=1. A random algorithm has AUC=0.5. Higher AUC doesn t mean all performance measures are better. 33 Lessons for evaluating ML algorithms Always compare to a simple baseline: In classification: Classify all samples as the majority class. Classify with a threshold on a single variable. In regression: Predict the average of the output for all samples. Compare to a simple linear regression. Use K-fold cross validation to properly estimate the error. If necessary, use a validation set to estimate hyper-parameters. Consider appropriate measures for fully characterizing the performance: Accuracy, Precision, Recall, F1, AUC

18 What you should know Understand the concepts of loss, error function, bias, variance. Commit to correctly applying cross-validation. Understand the common measures of performance. Know how to produce and read ROC curves. Understand the use of bootstrapping. Be concerned about good practices for machine learning! Read this paper today! K. Wagstaff, Machine Learning that Matters, ICML

### 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

### 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

### Performance Metrics. number of mistakes total number of observations. err = p.1/1

p.1/1 Performance Metrics The simplest performance metric is the model error defined as the number of mistakes the model makes on a data set divided by the number of observations in the data set, err =

### 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

### Data Mining Practical Machine Learning Tools and Techniques

Counting the cost Data Mining Practical Machine Learning Tools and Techniques Slides for Section 5.7 In practice, different types of classification errors often incur different costs Examples: Loan decisions

### Cross Validation. Dr. Thomas Jensen Expedia.com

Cross Validation Dr. Thomas Jensen Expedia.com About Me PhD from ETH Used to be a statistician at Link, now Senior Business Analyst at Expedia Manage a database with 720,000 Hotels that are not on contract

### 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

### Overview. Evaluation Connectionist and Statistical Language Processing. Test and Validation Set. Training and Test Set

Overview Evaluation Connectionist and Statistical Language Processing Frank Keller keller@coli.uni-sb.de Computerlinguistik Universität des Saarlandes training set, validation set, test set holdout, stratification

### Performance Measures in Data Mining

Performance Measures in Data Mining Common Performance Measures used in Data Mining and Machine Learning Approaches L. Richter J.M. Cejuela Department of Computer Science Technische Universität München

### 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

### 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

### Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Lecture 15 - ROC, AUC & Lift Tom Kelsey School of Computer Science University of St Andrews http://tom.home.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-17-AUC

### W6.B.1. FAQs CS535 BIG DATA W6.B.3. 4. If the distance of the point is additionally less than the tight distance T 2, remove it from the original set

http://wwwcscolostateedu/~cs535 W6B W6B2 CS535 BIG DAA FAQs Please prepare for the last minute rush Store your output files safely Partial score will be given for the output from less than 50GB input Computer

### 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

### Performance Measures for Machine Learning

Performance Measures for Machine Learning 1 Performance Measures Accuracy Weighted (Cost-Sensitive) Accuracy Lift Precision/Recall F Break Even Point ROC ROC Area 2 Accuracy Target: 0/1, -1/+1, True/False,

### 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

### The Reusable Holdout: Preserving Statistical Validity in Adaptive Data Analysis

The Reusable Holdout: Preserving Statistical Validity in Adaptive Data Analysis Moritz Hardt IBM Research Almaden Joint work with Cynthia Dwork, Vitaly Feldman, Toni Pitassi, Omer Reingold, Aaron Roth

### 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

### 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

### T-61.3050 : Email Classification as Spam or Ham using Naive Bayes Classifier. Santosh Tirunagari : 245577

T-61.3050 : Email Classification as Spam or Ham using Naive Bayes Classifier Santosh Tirunagari : 245577 January 20, 2011 Abstract This term project gives a solution how to classify an email as spam or

### Cross-Validation. Synonyms Rotation estimation

Comp. by: BVijayalakshmiGalleys0000875816 Date:6/11/08 Time:19:52:53 Stage:First Proof C PAYAM REFAEILZADEH, LEI TANG, HUAN LIU Arizona State University Synonyms Rotation estimation Definition is a statistical

### MACHINE LEARNING IN HIGH ENERGY PHYSICS

MACHINE LEARNING IN HIGH ENERGY PHYSICS LECTURE #1 Alex Rogozhnikov, 2015 INTRO NOTES 4 days two lectures, two practice seminars every day this is introductory track to machine learning kaggle competition!

### Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

### Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be

### 1. Classification problems

Neural and Evolutionary Computing. Lab 1: Classification problems Machine Learning test data repository Weka data mining platform Introduction Scilab 1. Classification problems The main aim of a classification

### 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,

### Machine Learning. Term 2012/2013 LSI - FIB. Javier Béjar cbea (LSI - FIB) Machine Learning Term 2012/2013 1 / 34

Machine Learning Javier Béjar cbea LSI - FIB Term 2012/2013 Javier Béjar cbea (LSI - FIB) Machine Learning Term 2012/2013 1 / 34 Outline 1 Introduction to Inductive learning 2 Search and inductive learning

### 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

### Machine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer

Machine Learning Chapter 18, 21 Some material adopted from notes by Chuck Dyer What is learning? Learning denotes changes in a system that... enable a system to do the same task more efficiently the next

### Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

### 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

### Behavior Analysis of SVM Based Spam Filtering Using Various Kernel Functions and Data Representations

ISSN: 2278-181 Vol. 2 Issue 9, September - 213 Behavior Analysis of SVM Based Spam Filtering Using Various Kernel Functions and Data Representations Author :Sushama Chouhan Author Affiliation: MTech Scholar

### Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus

Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus 1. Introduction Facebook is a social networking website with an open platform that enables developers to extract and utilize user information

### Introduction to Machine Learning

Introduction to Machine Learning Prof. Alexander Ihler Prof. Max Welling icamp Tutorial July 22 What is machine learning? The ability of a machine to improve its performance based on previous results:

### 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

### Active Learning SVM for Blogs recommendation

Active Learning SVM for Blogs recommendation Xin Guan Computer Science, George Mason University Ⅰ.Introduction In the DH Now website, they try to review a big amount of blogs and articles and find the

### A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions

A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions Jing Gao Wei Fan Jiawei Han Philip S. Yu University of Illinois at Urbana-Champaign IBM T. J. Watson Research Center

### 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

### We discuss 2 resampling methods in this chapter - cross-validation - the bootstrap

Statistical Learning: Chapter 5 Resampling methods (Cross-validation and bootstrap) (Note: prior to these notes, we'll discuss a modification of an earlier train/test experiment from Ch 2) We discuss 2

### Random Forest Based Imbalanced Data Cleaning and Classification

Random Forest Based Imbalanced Data Cleaning and Classification Jie Gu Software School of Tsinghua University, China Abstract. The given task of PAKDD 2007 data mining competition is a typical problem

### Performance Metrics for Graph Mining Tasks

Performance Metrics for Graph Mining Tasks 1 Outline Introduction to Performance Metrics Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical

### MACHINE LEARNING. Introduction. Alessandro Moschitti

MACHINE LEARNING Introduction Alessandro Moschitti Department of Computer Science and Information Engineering University of Trento Email: moschitti@disi.unitn.it Course Schedule Lectures Tuesday, 14:00-16:00

### 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

### 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

### Data Clustering. Dec 2nd, 2013 Kyrylo Bessonov

Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms k-means Hierarchical Main

### 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

### Maschinelles Lernen mit MATLAB

Maschinelles Lernen mit MATLAB Jérémy Huard Applikationsingenieur The MathWorks GmbH 2015 The MathWorks, Inc. 1 Machine Learning is Everywhere Image Recognition Speech Recognition Stock Prediction Medical

### Introduction to Applied Supervised Learning w/nlp

Introduction to Applied Supervised Learning w/nlp Stephen Purpura Cornell University Department of Information Science Talk at the Tools for Text Workshop June 2010 Topics Transition from Unsupervised

### Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

### 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

### 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

### Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms

Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms Scott Pion and Lutz Hamel Abstract This paper presents the results of a series of analyses performed on direct mail

### Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction

Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction Huanjing Wang Western Kentucky University huanjing.wang@wku.edu Taghi M. Khoshgoftaar

### More Data Mining with Weka

More Data Mining with Weka Class 5 Lesson 1 Simple neural networks Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz Lesson 5.1: Simple neural networks Class

### Predicting Flight Delays

Predicting Flight Delays Dieterich Lawson jdlawson@stanford.edu William Castillo will.castillo@stanford.edu Introduction Every year approximately 20% of airline flights are delayed or cancelled, costing

### 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

### 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

### Predictive Data modeling for health care: Comparative performance study of different prediction models

Predictive Data modeling for health care: Comparative performance study of different prediction models Shivanand Hiremath hiremat.nitie@gmail.com National Institute of Industrial Engineering (NITIE) Vihar

### Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones

Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones http://www.mpia.de/homes/calj/mlpr_mpia2008.html 1 1 What is machine learning? Data description and interpretation

### Mining the Software Change Repository of a Legacy Telephony System

Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa,

### Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data

CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear

### Evaluation of selected data mining algorithms implemented in Medical Decision Support Systems Kamila Aftarczuk

Master Thesis Software Engineering Thesis no: MSE-2007-21 September 2007 Evaluation of selected data mining algorithms implemented in Medical Decision Support Systems Kamila Aftarczuk This thesis is submitted

### 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

### 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

### 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

### Consistent Binary Classification with Generalized Performance Metrics

Consistent Binary Classification with Generalized Performance Metrics Nagarajan Natarajan Joint work with Oluwasanmi Koyejo, Pradeep Ravikumar and Inderjit Dhillon UT Austin Nov 4, 2014 Problem and Motivation

### Experiments in Web Page Classification for Semantic Web

Experiments in Web Page Classification for Semantic Web Asad Satti, Nick Cercone, Vlado Kešelj Faculty of Computer Science, Dalhousie University E-mail: {rashid,nick,vlado}@cs.dal.ca Abstract We address

### 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

### Heart Disease Prediction System Using Data Mining Techniques

ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY An International Open Free Access, Peer Reviewed Research Journal Published By: Oriental Scientific Publishing Co., India. www.computerscijournal.org ISSN:

### 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

### Microsoft Azure Machine learning Algorithms

Microsoft Azure Machine learning Algorithms Tomaž KAŠTRUN @tomaz_tsql Tomaz.kastrun@gmail.com http://tomaztsql.wordpress.com Our Sponsors Speaker info https://tomaztsql.wordpress.com Agenda Focus on explanation

### 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,

### 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

### ROC Curve, Lift Chart and Calibration Plot

Metodološki zvezki, Vol. 3, No. 1, 26, 89-18 ROC Curve, Lift Chart and Calibration Plot Miha Vuk 1, Tomaž Curk 2 Abstract This paper presents ROC curve, lift chart and calibration plot, three well known

### UNDERSTANDING THE EFFECTIVENESS OF BANK DIRECT MARKETING Tarun Gupta, Tong Xia and Diana Lee

UNDERSTANDING THE EFFECTIVENESS OF BANK DIRECT MARKETING Tarun Gupta, Tong Xia and Diana Lee 1. Introduction There are two main approaches for companies to promote their products / services: through mass

### Leak Detection Theory: Optimizing Performance with MLOG

Itron White Paper Water Loss Management Leak Detection Theory: Optimizing Performance with MLOG Rich Christensen Vice President, Research & Development 2009, Itron Inc. All rights reserved. Introduction

### COMP-424: Artificial intelligence. Lecture 7: Game Playing

COMP 424 - Artificial Intelligence Lecture 7: Game Playing Instructor: Joelle Pineau (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/comp424 Unless otherwise noted, all material posted

### Universiteit Leiden Opleiding Informatica & Economie

Universiteit Leiden Opleiding Informatica & Economie Fully Automatic Machine Learning: Hyper-parameter Optimization and Model Selection with Scikit-learn Rachelle Blok 02/09/15 Siegfried Nijssen Erik Schultes

### Part III: Machine Learning. CS 188: Artificial Intelligence. Machine Learning This Set of Slides. Parameter Estimation. Estimation: Smoothing

CS 188: Artificial Intelligence Lecture 20: Dynamic Bayes Nets, Naïve Bayes Pieter Abbeel UC Berkeley Slides adapted from Dan Klein. Part III: Machine Learning Up until now: how to reason in a model and

### Introduction to Statistical Machine Learning

CHAPTER Introduction to Statistical Machine Learning We start with a gentle introduction to statistical machine learning. Readers familiar with machine learning may wish to skip directly to Section 2,

### lop Building Machine Learning Systems with Python en source

Building Machine Learning Systems with Python Master the art of machine learning with Python and build effective machine learning systems with this intensive handson guide Willi Richert Luis Pedro Coelho

### 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

### Predict Influencers in the Social Network

Predict Influencers in the Social Network Ruishan Liu, Yang Zhao and Liuyu Zhou Email: rliu2, yzhao2, lyzhou@stanford.edu Department of Electrical Engineering, Stanford University Abstract Given two persons

### 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

### Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification

Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification Tina R. Patil, Mrs. S. S. Sherekar Sant Gadgebaba Amravati University, Amravati tnpatil2@gmail.com, ss_sherekar@rediffmail.com

### An Evaluation of Machine Learning-based Methods for Detection of Phishing Sites

An Evaluation of Machine Learning-based Methods for Detection of Phishing Sites Daisuke Miyamoto, Hiroaki Hazeyama, and Youki Kadobayashi Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma,

### 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

### FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN-BASED KNOWLEDGE-DISCOVERY PROCESS

FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN-BASED KNOWLEDGE-DISCOVERY PROCESS Breno C. Costa, Bruno. L. A. Alberto, André M. Portela, W. Maduro, Esdras O. Eler PDITec, Belo Horizonte,

### K-nearest-neighbor: an introduction to machine learning

K-nearest-neighbor: an introduction to machine learning Xiaojin Zhu jerryzhu@cs.wisc.edu Computer Sciences Department University of Wisconsin, Madison slide 1 Outline Types of learning Classification:

### PCA, Clustering and Classification. By H. Bjørn Nielsen strongly inspired by Agnieszka S. Juncker

PCA, Clustering and Classification By H. Bjørn Nielsen strongly inspired by Agnieszka S. Juncker Motivation: Multidimensional data Pat1 Pat2 Pat3 Pat4 Pat5 Pat6 Pat7 Pat8 Pat9 209619_at 7758 4705 5342

### Classification using Logistic Regression

Classification using Logistic Regression Ingmar Schuster Patrick Jähnichen using slides by Andrew Ng Institut für Informatik This lecture covers Logistic regression hypothesis Decision Boundary Cost function

### SVM Ensemble Model for Investment Prediction

19 SVM Ensemble Model for Investment Prediction Chandra J, Assistant Professor, Department of Computer Science, Christ University, Bangalore Siji T. Mathew, Research Scholar, Christ University, Dept of

### Predicting air pollution level in a specific city

Predicting air pollution level in a specific city Dan Wei dan1991@stanford.edu 1. INTRODUCTION The regulation of air pollutant levels is rapidly becoming one of the most important tasks for the governments

### Machine Learning in Spam Filtering

Machine Learning in Spam Filtering A Crash Course in ML Konstantin Tretyakov kt@ut.ee Institute of Computer Science, University of Tartu Overview Spam is Evil ML for Spam Filtering: General Idea, Problems.

### Logistic Regression for Spam Filtering

Logistic Regression for Spam Filtering Nikhila Arkalgud February 14, 28 Abstract The goal of the spam filtering problem is to identify an email as a spam or not spam. One of the classic techniques used

### On Cross-Validation and Stacking: Building seemingly predictive models on random data

On Cross-Validation and Stacking: Building seemingly predictive models on random data ABSTRACT Claudia Perlich Media6 New York, NY 10012 claudia@media6degrees.com A number of times when using cross-validation

### Fraud Detection for Online Retail using Random Forests

Fraud Detection for Online Retail using Random Forests Eric Altendorf, Peter Brende, Josh Daniel, Laurent Lessard Abstract As online commerce becomes more common, fraud is an increasingly important concern.

### Applying Data Science to Sales Pipelines for Fun and Profit

Applying Data Science to Sales Pipelines for Fun and Profit Andy Twigg, CTO, C9 @lambdatwigg Abstract Machine learning is now routinely applied to many areas of industry. At C9, we apply machine learning