WEKA: A Practical Machine Learning Tool WEKA ： A Practical Machine Learning Tool

WEKA: A Practical Machine Learning Tool Contents  1.Introduction to Weka  2.Explorer  3.Other three main tools  4.Conclusions  5.Reference

WEKA: A Practical Machine Learning Tool Introduction – What is Weka?  In nature: A flightless bird with an inquisitive nature found only on the islands of New Zealand.  Actually: A practical machine learning tool developed by the University of Waikato in New Zealand. It is short for Waikato Environment for Knowledge Analysis.  Definition: A collection of machine learning algorithms for data mining tasks.  Language: It is written in Java and runs on almost any platform.  Usage: The algorithms can either be applied: (1) directly to a dataset (without writing any codes); (2) called from your own Java code.

WEKA: A Practical Machine Learning Tool Introduction – Weka consists of  Explorer  Experimenter  Knowledge flow  Simple Command Line Interface(CLI)  Other tools and Visualization  Java interface

WEKA: A Practical Machine Learning Tool Explorer  WEKA’s main graphical user interface  Gives access to all its facilities using menu selection and form filling.(Data-Preprocess/Classify/Cluster/Associate/Select Attributes/Visualize) 1.Data 2. Operations of Explorer with a Classification example.

WEKA: A Practical Machine Learning Tool Explorer – Data(1)  From files: CSV, ARFF, C4.5… （ no *.xls ）  Data loaded from URL or DB *.xls *.csv Attribute-Class Attribute Instance Instances Tips ： weather.arff ( C:/Program Files/Weka/data/ )

WEKA: A Practical Machine Learning Tool Explorer – Data(2) ARFF(Attribute-Relation File Format) ① numeric (real or integer numbers) ② ③ string ④ date [ ]  % notes More details: ~ml/weka/arff.html

WEKA: A Practical Machine Learning Tool Explorer – Operations with an example Input data  Data preprocess  Choose classifier  Test options  Run  Result analysis

WEKA: A Practical Machine Learning Tool Explorer Input data Summary Statistics Select an attribute Visualization

WEKA: A Practical Machine Learning Tool Explorer Tune Parameters Select a Filter Weka Filter Apply the Filter

WEKA: A Practical Machine Learning Tool Explorer Tune Parameters Select a Classifier Decide how to evaluate Model list Results

WEKA: A Practical Machine Learning Tool Right-click on model to get Menu (save, visualize, etc)

WEKA: A Practical Machine Learning Tool

Others – Experimenter  Comparing different learning algorithms  on different datasets  with various parameter settings  and analyzing the performance statistics Click it for Experimenter

WEKA: A Practical Machine Learning Tool Others – KnowledgeFlow  The KnowledgeFlow provides an alternative to the Explorer as a graphical front end to Weka's core algorithms.  The KnowledgeFlow is a work in progress so some of the functionality from the Explorer is not yet available. Click it for KnowledgyFlow

WEKA: A Practical Machine Learning Tool Others – Simple command line interface  All implementations of the algorithms have a uniform command- line interface.  java weka.classifiers.trees.J48 -t weather.arff Click it for Simple CLI

WEKA: A Practical Machine Learning Tool Conclusions 1.Explorer: Input data  Data preprocess  Choose classifier  Test options  Run  Result analysis 2.Experimenter: It is necessary for further studies. 3.Make full use of ：  1. Java tips;  2. WekaManual.pdf; (C:/Program Files/Weka/ )  3. Play it yourself!

WEKA: A Practical Machine Learning Tool Reference  Mitchell, T. Machine Learning, 1997 McGraw Hill.  Ian H. Witten, Eibe Frank, Len Trigg, Mark Hall, Geoffrey Holmes, and Sally Jo Cunningham (1999). Weka: Practical machine learning tools and techniques with Java implementations.  Ian H. Witten, Eibe Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques (Second Edition, 2005). San Francisco: Morgan Kaufmann  Weka Homepage:  Wekawiki:  Weka on SourceForge.net:  WekaManual.pdf (C:\Program Files\Weka-3-6\WekaManual.pdf)