Transcript YALE
UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY TIES443 Tutorial 1 Prototyping DM Techniques with WEKA and YALE Open-Source Software Mykola Pechenizkiy Course webpage: http://www.cs.jyu.fi/~mpechen/TIES443 November 7, 2006 Department of Mathematical Information Technology University of Jyväskylä TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 1 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Contents • Brief Review of DM Software – Commercial – Open-source • WEKA http://www.cs.waikato.ac.nz/~ml/weka/index.html • YALE http://rapid-i.com/ • The R Project for Statistical Computing http://www.r-project.org/ • Pentaho – whole BI solutions. http://www.pentaho.com/ – Matlab – Sami will tell you more during the 2nd Tutorial • WEKA vs. YALE Comparison – Exploration – Experimentation – Visualization • 1st Assignment http://www.cs.jyu.fi/~mpechen/TIES443/tutorials/assignment1.pdf TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 2 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Data Mining Software • Many providers of commercial DM software – SAS Enterprise Miner, SPSS Clementine, Statistica Data Miner, MS SQL Server, Polyanalyst, KnowledgeSTUDIO, … – IBM Intelligent Miner. • Universities can now receive free copies of DB2 and Intelligent Miner for educational or research purposes. – See http://www.kdnuggets.com/software/suites.html for a list • Open Source: – WEKA (Waikato Environment for Knowledge Analysis) – YALE (Yet Another Learning Environment) – Many others • MLC++, Minitab, AlphaMiner, Rattle, KNIME – The Pentaho BI project – • “a pioneering initiative by the Open Source development community to provide organizations with a comprehensive set of BI capabilities that enable them to radically improve business performance, efficiency, and effectiveness.” TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 3 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Data Mining with WEKA The following slides are from http://prdownloads.sourceforge.net/weka/weka.ppt by Eibe Frank Copyright: Martin Kramer ([email protected]) TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 4 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY WEKA: the software • Machine learning/data mining software written in Java (distributed under the GNU Public License) • Used for research, education, and applications • Complements “Data Mining” book by Witten & Frank – http://www.cs.waikato.ac.nz/~ml/weka/book.html • Main features: – Comprehensive set of data pre-processing tools, learning algorithms and evaluation methods – Graphical user interfaces (incl. data visualization) – Environment for comparing learning algorithms TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 5 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY WEKA only deals with “flat” files @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present ... TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 6 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY WEKA only deals with “flat” files @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present ... TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 7 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 8 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Command line tutorial http://weka.sourceforge.net/wekadoc/index.php/en%3APrimer TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 9 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 10 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Explorer: Pre-processing the Data • Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary • Data can also be read from a URL or from an SQL database (using JDBC) • Pre-processing tools in WEKA are called “filters” • WEKA contains filters for: – Discretization, normalization, resampling, attribute selection, transforming and combining attributes, … TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 11 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 12 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 13 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 14 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 15 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 16 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 17 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 18 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 19 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 20 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 21 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 22 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 23 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 24 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 25 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 26 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 27 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 28 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 29 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 30 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 31 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 32 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Explorer: building “classifiers” • Classifiers in WEKA are models for predicting nominal or numeric quantities • Implemented learning schemes include: – Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, … • “Meta”-classifiers include: – Bagging, boosting, stacking, error-correcting output codes, locally weighted learning, … TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 33 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 34 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 35 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 36 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 37 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 38 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 39 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 40 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 41 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 42 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 43 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 44 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 45 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 46 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 47 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 48 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 49 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 50 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 51 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 52 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 53 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 54 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 55 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 56 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 57 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 58 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 59 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 60 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 61 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 62 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 63 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 64 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 65 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY QuickTime™ and a TI FF (LZW) decompressor are needed to see this picture. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 66 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY QuickTime™ and a TI FF (LZW) decompressor are needed to see this picture. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 67 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY QuickTime™ and a TI FF (LZW) decompressor are needed to see this picture. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 68 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 69 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 70 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 71 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 72 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 73 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 74 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 75 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 76 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 77 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 78 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 79 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 80 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Explorer: clustering data • WEKA contains “clusterers” for finding groups of similar instances in a dataset • Implemented schemes are: – k-Means, EM, Cobweb, X-means, FarthestFirst • Clusters can be visualized and compared to “true” clusters (if given) • Evaluation based on loglikelihood if clustering scheme produces a probability distribution TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 81 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 82 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 83 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 84 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 85 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 86 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 87 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 88 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 89 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 90 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 91 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 92 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 93 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 94 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 95 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 96 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Explorer: finding associations • WEKA contains an implementation of the Apriori algorithm for learning association rules – Works only with discrete data • Can identify statistical dependencies between groups of attributes: – milk, butter bread, eggs (with confidence 0.9 and support 2000) • Apriori can compute all rules that have a given minimum support and exceed a given confidence TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 97 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 98 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 99 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 100 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 101 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 102 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 103 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 104 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Explorer: attribute selection • Panel that can be used to investigate which (subsets of) attributes are the most predictive ones • Attribute selection methods contain two parts: – A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking – An evaluation method: correlation-based, wrapper, information gain, chi-squared, … • Very flexible: WEKA allows (almost) arbitrary combinations of these two TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 105 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 106 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 107 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 108 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 109 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 110 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 111 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 112 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 113 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Explorer: Data Visualization • Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem • WEKA can visualize single attributes (1-d) and pairs of attributes (2-d) – To do: rotating 3-d visualizations (Xgobi-style) • Color-coded class values • “Jitter” option to deal with nominal attributes (and to detect “hidden” data points) • “Zoom-in” function TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 114 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 115 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 116 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 117 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 118 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 119 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 120 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 121 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 122 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 123 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 124 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 125 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 126 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Performing Experiments • Experimenter makes it easy to compare the performance of different learning schemes • For classification and regression problems • Results can be written into file or database • Evaluation options: cross-validation, learning curve, hold- out • Can also iterate over different parameter settings • Significance-testing built in! TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 127 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 128 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 129 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 130 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 131 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 132 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 133 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 134 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 135 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 136 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 137 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 138 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 139 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 140 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY The Knowledge Flow GUI • New graphical user interface for WEKA • Java-Beans-based interface for setting up and running machine learning experiments • Data sources, classifiers, etc. are beans and can be connected graphically • Data “flows” through components: e.g., “data source” -> “filter” -> “classifier” -> “evaluator” • Layouts can be saved and loaded again later TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 141 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 142 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 143 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 144 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 145 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 146 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 147 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 148 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 149 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 150 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 151 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 152 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 153 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 154 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 155 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 156 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 157 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 158 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 159 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 160 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 161 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Conclusion: try it yourself! • WEKA is available at http://www.cs.waikato.ac.nz/ml/weka Also has a list of projects based on WEKA YALE has different interfaces and ideas behind but it also integrates all available DM techniques from WEKA TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 162 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY The following slides are compiled from screenshots and related descriptions available from YALE pages http://rapid-i.com/ YALE – Yet Another Learning Environment Artificial Intelligence Unit of the University of Dortmund. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 163 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Features of YALE • freely available open-source knowledge discovery environment • 100% pure Java (runs on every major platform and operating system) • KD processes are modeled as simple operator trees which is both intuitive and powerful • operator trees or subtrees can be saved as building blocks for later re-use • internal XML representation ensures standardized interchange format of data mining experiments • simple scripting language allowing for automatic largescale experiments • multi-layered data view concept ensures efficient and transparent data handling TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 164 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Features of YALE • Flexibility in using YALE: – graphical user interface (GUI) for interactive prototyping – command line mode (batch mode) for automated large-scale applications – Java API to ease usage of YALE from your own programs • simple plugin and extension mechanisms, some plugins already exists and you can easily add your own • powerful plotting facility offering a large set of sophisticated highdimensional visualization techniques for data and models • more than 350 machine learning, evaluation, in- and output, pre- and post-processing, and visualization operators plus numerous meta optimization schemes • machine learning library WEKA fully integrated • YALE’s potential application include text mining, multimedia mining, feature engineering, data stream mining and tracking drifting concepts, development of ensemble methods, and distributed data mining. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 165 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Experiment Setup the initial operator tree which only consist of a root node. The "Tree View" tab is the most often used editor for YALE experiments. Left: the current operator tree. Right: a table with the parameters of the currently selected operator. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE The lower part of the YALE main frame serves for displaying and viewing log and error messages. 166 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY After the learning operator "J48", a breakpoint indicates that the intermediate results can be inspected. Due to the modular concept of YALE, it is always possible to inspect and save intermediate results, e.g. the results for each individual run in a cross validation TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 167 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY add new operators to the experiment: • directly from the context menu of its parent. •the new operator dialog shown in this screenshot. Several search constrains exist and a short description for each operator is shown TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 168 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY The operator trees are coded and represented by a simple XML format. The XML editor tab allows for fast and direct manipulations of the current experiment. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 169 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY All views can also be printed and exported to a wide range of graphic formats including jpg, png, ps and pdf. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE The "Box View" - is another viewer for YALE experiments. • the box format is an intuitive way of representing the nesting of the operators. • but editing is not possible 170 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY "Monitor" tab provides an overview of the currently used memory and is an important tool for large-scale data mining tasks on huge data sets. The amount of used memory during an experiment run can even be logged in the same way like all other provided logging values. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 171 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Data can be imported from several file formats with the attribute editor. Other file formats like Arff, C45, csv, and dBase can be loaded with specialized operators. Attribute Editor can be used to create meta data descriptions from almost arbitrary file formats. These meta data descriptions can then be used for an input operator which actually loads the data. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 172 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Additional attributes (features) can easily be constructed from your data. YALE provides several approaches to construct the best feature space automatically. These approaches range from feature space transformations like PCA, GHA, ICA or the kernel versions to standard feature selection techniques to several evolutionary approaches for feature construction and extraction. Tutorial 1: Introduction to WEKA and YALE 173 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Help features to ease the learning phase for new users: • An online tutorial, • tool tip texts, • a beginner and expert mode, operator info screens, • a GUI manual, • and the YALE tutorial. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 174 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Data Visualization Each time a data set is presented in the results tab (e.g. after loading it), several views appear: a meta data view describing all attributes, a data view showing the actual data and a plot view providing a large set of (high-dimensional) plotters for the data set at hand. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 175 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY The basic scatter plotter: Two of the attribute are used as axes, the class label attribute is used for colorization. The legend at the top maps the colors used to the classes or, in case of a real-valued color plot column, to the corresponding real values. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 176 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY The standard scatter plotter even allows jittering, zooming, and displaying example ids. Doubleclicking a data point opens a visualizer. The standard example visualizer is presented here. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 177 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY 2D scatter plots can be put together to a scatter plot matrix where for all pairs of dimensions a usual scatter plot is drawn. This plotter is only available for less then 10 dimensions. For higher number of dimensions one of the other high-dimensional data plotter presented below should be used. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 178 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY A 3D scatter plot exists similar to the colorized 2D scatter plot discussed above. The viewport can be rotated and zoomed to fit your needs. The built-in 2D and 3D plotters are a quick and easy way to view your numerical and nominal results, even as online plot at experiment runtime! TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 179 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY SOM (Self-Organizing Map) plotter which uses a Kohonen net for dimensionality reduction. Plotting of the U-, the P-, and the U*-Matrix are supported with different color schemes. The data points can be colorized by one of the data columns, e.g. with the prediction label. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 180 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY SOM (Self-Organizing Map) plotter which uses a Kohonen net for dimensionality reduction. a gray scale color scheme was used to plot the UMatrix. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 181 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY The parallel plotter prints the axes of all dimensions parallel to each other. This is the natural visualization technique for series data but can also be useful for other types of data. The main advantage of parallel plots is that a very high number of dimensions can be visualized with this technique. The dimensions are colorized with the feature weights. The more yellow a dimension is marked, the more important this column is. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 182 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY quartile plots (also known as box plots) are often used for experiment results like performance values but it is possible to summarize the statistical properties of data columns in general with this type of plot. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 183 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Histogram plots (also known as distribution plots) TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 184 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY RadViz is another highdimensional data plotter where the data columns are placed as radial dimension anchors. Each data point is connected to each anchor with a spring corresponding to the feature values. This will lead to a fixed position in the two-dimensional plane. Again, weights are used to mark the more important columns. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 185 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY A survey plot is a sort of vertical histogram matrix also suitable for a large number of dimensions. Each line corresponds to one data point and can be colorized by one of the columns. The length of each section corresponds to the value of the data point for that dimension. For up to three dimensions the order of the histograms can be selected. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 186 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Andrews curves are another way of visualizing highdimensional data. Each data point is projected onto a set of orthogonal trigonometric functions and displayed as a curve. It is known that Andrews curves preserve distances, so they have many uses for data analysis and exploration. Often outliers and hidden patterns can be well detected in these plots. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 187 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Visualization of Models and other Results The result of a learning step is called model. Some models provide a graphical representation of the learned hypothesis. This screenshot presents a learned decision tree for the widely known "labor negotiations" data set from the UCI repository. Results like learned models, performance values, data sets or selected attributes are displayed when the experiment is completed or a breakpoint is reached TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 188 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY In cases where no graphical representation of a learned model is available, at least a textual description of the learned model is presented. In this screenshot you see a Stacking model consisting of a rule model (the upper half) and a neural network (starts at the lower half). Both base models are described by simple and understandable texts. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 189 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY This is a density plot (similar to a contour plot) of the decision function of a Support Vector Machine (SVM). Almost all SVM implementations in YALE provide a table and a plot view of the learned model. In this screenshot, red points refer to support vectors, blue points to normal training examples. Bluish regions will be predicted negative, reddish regions will be predicted positive. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 190 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY only the support vectors are shown colorized by the preditcted function value for the corresponding data point. Examples on the red side will be predicted positive; examples on the blue side will be predicted negative. There is a perfectly linear separation in two of the dimensions and it seems to be that the parameters were not chosen optimal since the number of support vectors is rather high. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 191 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY alpha values (Lagrange multipliers) of the SVM are plotted against the function values and colorized with the true label. We applied a slight jittering to make more points visible. This model seems to be "well-learned", since only few points have a alpha value not equal to zero and these are the points with function values approximately 0. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 192 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY This surface plot presents the result of a meta optimization experiment: the parameters of one of the operators are optimized. the plot can be rotated and zoomed. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 193 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 194 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 195 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 196 UNIVERSITY OF JYVÄSKYLÄ TIES443: Introduction to DM DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY Tutorial 1: Introduction to WEKA and YALE 197 UNIVERSITY OF JYVÄSKYLÄ DEPARTMENT OF MATHEMATICAL INFORMATION TECHNOLOGY WEKA & YALE Comparison • You tell me in your report • Now lets go through the first assignment • 1st Assignment http://www.cs.jyu.fi/~mpechen/TIES443/tutorials/assig nment1.pdf • My advise for you is to come back to this assignment and WEKA and YALE tools after each forthcoming lecture to see how the things are implemented and can be used in practice. TIES443: Introduction to DM Tutorial 1: Introduction to WEKA and YALE 198