Download presentation

Presentation is loading. Please wait.

Published byErick Eves Modified over 4 years ago

1
Florida International University COP 4770 Introduction of Weka

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

3
Whats 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 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 tour Getting 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 Filter Load Feature Info Label 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; AttributeSelection lets 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 tour 2D 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
Output Text-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
Output Text-based results - example classifyResultExample.txt Introduction of Weka

19
Output Graphical-based results Introduction of Weka

20
Any questions?? Introduction of Weka

Similar presentations

OK

Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.

Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google

Ppt on cost center accounting Ppt on tsunami in india Ppt on restaurant business plan Ppt on power system stability theory Ppt on second law of thermodynamics and evolution Ppt on various layers of the earth Ppt on national education policy 1986 super Ppt on conservation of momentum equation Ppt on merger acquisition and takeover Ppt on panel discussion invitation