Introduction of Weka

1. Introduction of Weka Florida International University COP 4770 Introduction of Weka

2. Outline • Introduction • Take a tour • Input & output format Introduction of Weka

3. What’s Weka • Waikato Environment for Knowledge Analysis (WEKA) • Developed by the Department of Computer Science, University of Waikato, New Zealand • Machine learning/data mining software written in Java (distributed under the GNU Public License) • Used for research, education, and applications Introduction of Weka

4. Weka Homepage • http://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/download Introduction of Weka

5. Installation Weka • To run: • weka-3-6-3.exe Introduction of Weka

6. Main 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 Cobweb • Schemes for feature selection: • Ranker…. Introduction of Weka

7. Take a tourGetting start • Start  All Programs  Weka 3.6.3  Weka 3.6 Click to Start a Tour! Introduction of Weka

8. Take a tour Weka Explorer Screenshot Load Filter Label Info Feature Info Introduction of Weka

9. Take a tour • Click “Open file” ; • Choose “Weka-3.6/data/*.arff”; • Click “Open”. Introduction of Weka

10. Take a tour Filter • Filters can be used to change data files; • AttributeSelectionlets you select a set of attributes; • Other filters Discretize: 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 Weka

11. Take a tour2D Visualization Visualize Attributes Introduction of Weka

12. Take a tour Classifier - 1 Introduction of Weka

13. Take a tour Classifier - 2 Single Click! Introduction of Weka

14. Take a tour Classifier - 3 Introduction of Weka

15. Input File: .arff Format • Detail: • http://www.cs.waikato.ac.nz/~ml/weka/arff.html • Require 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 segment Introduction of Weka

16. Input File: .cvs Format Introduction of Weka

17. OutputText-based results • Run 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 Weka

18. OutputText-based results - example • classifyResultExample.txt Introduction of Weka

19. OutputGraphical-based results Introduction of Weka

20. Any questions?? Introduction of Weka