Introduction of WEKA

Florida International UniversityCOP 4770Introduction of Weka Introduction of Weka1OutlineIntroductionTake a tourInput & output format

Introduction of WekaWhats WekaWaikato Environment for Knowledge Analysis (WEKA)Developed by the Department of Computer Science, University of Waikato, New ZealandMachine learning/data mining software written in Java (distributed under the GNU Public License)Used for research, education, and applications

Introduction of WekaWeka Homepagehttp://www.cs.waikato.ac.nz/ml/weka/

To download WEKA 3.6.3:http://sourceforge.net/projects/weka/files/weka-3-6-windows/3.6.3/weka-3-6-3.exe/downloadIntroduction of WekaInstallation WekaTo run:weka-3-6-3.exeIntroduction of WekaMain Features Schemes for classification include: decision trees, rule learners, naive Bayes, decision tables, locally weighted regression, SVMs, instance-based learners, logistic regression, voted perceptrons, multi-layer perceptron Schemes for numeric prediction include: linear regression, model tree generators, locally weighted regression, instance-based learners, decision tables, multi-layer perceptron Meta-schemes include: Bagging, boosting, stacking, regression via classification, classification via regression, cost sensitive classification Schemes for clustering:EM and CobwebSchemes for feature selection:Ranker.Introduction of WekaTake a tourGetting startStart All Programs Weka 3.6.3 Weka 3.6

Click to Start a Tour!Introduction of WekaTake a tour Weka Explorer Screenshot

FilterLoadFeature InfoLabel InfoIntroduction of WekaTake a tourClick Open file ;Choose Weka-3.6/data/*.arff;Click Open. Introduction of WekaTake a tour FilterFilters can be used to change data files;AttributeSelection lets you select a set of attributes;

Other filtersDiscretize: Discretizes a range of numeric attributes in the dataset into nominal attributes;NominalToBinary: Converts nominal attributes into binary ones, replacing each attribute with k values with k-1 new binary attributes;

Introduction of WekaTake a tour2D Visualization

Visualize AttributesIntroduction of WekaTake a tour Classifier - 1

Introduction of WekaTake a tour Classifier - 2

Single Click!Introduction of WekaTake a tour Classifier - 3

Introduction of WekaInput File: .arff Format Detail: http://www.cs.waikato.ac.nz/~ml/weka/arff.htmlRequire declarations of @RELATION, @ATTRIBUTE and @DATA

@RELATION declaration associates a name with the dataset@ATTRIBUTE declaration specifies the name and type of an attribute@DATA declaration is a single line denoting the start of the data segmentIntroduction of WekaInput File: .cvs Format

Introduction of WekaOutputText-based resultsRun Information;Summary of model;Statistics of training data;Predictions of test data;Type of sampling;Confusing Matrix;Detailed Accuracy by class;Entropy evaluation measures;Introduction of WekaOutputText-based results - exampleclassifyResultExample.txtIntroduction of WekaOutputGraphical-based results

Introduction of WekaAny questions??Introduction of Weka