Weka 사용법 - Welcome to SNU Biointelligence Lab!!€¦ · PPT file · Web view ·...

Introduction to WekaML Seminar for Rookies

2012-02-03Byoung-Hee Kim

Biointelligence Lab, Seoul National University

(Predictive) Analytics

Data Mining

Machine Learning

Hype Cycle ofHype Cycle of Emerging Technologies 2010, GartnerEmerging Technologies 2010, Gartner

Analytics as a Mainstream Technology

Components of Data Mining

Weka as a Must-Have Tool

I use Weka constantly in my speech work. It is the first thing a reach for when encountering a new problem. What a terrific tool.

A must for anyone even marginally interested in machine learning and classification techniques.

One of the most useful AI software packages available. It's only serious flaw is being infected with the GNU virus.

Reviews in Sourceforge.net

Agenda

General Information on Weka

Weka: Data Mining Software in Java Weka is a collection of machine learning algorithms for

data mining & machine learning tasks What you can do with Weka are

data pre-processing, feature selection, classification, regression, clustering, association rules, and visualization

Weka is an open source software issued under the GNU General Public License

How to get? http://www.cs.waikato.ac.nz/ml/weka/ or just type ‘Weka’ in google.

Components of Weka

Explorer lets you do various data mining tasks in interactive, step-by-step way.The first choice, usually

KnowledgeFlow allows you to design configurations for streamed data processing

Experimenter allows you to classification and regression in batch way-Different parameter settings-Various datasets-Comparison of models-Large-scale statistical experiments

Simple CLI lies behind other interfaces. By entering textual commands, you can access to all features of the Weka system.

Auxiliary Tools in the menu

Practice: Classifying Iris Flower

Iris virginicaIris versicolorIris setosa

Features for Classification

Practice: Classifying Iris Flower

Terminology

Neural Networks

MLP (Multilayer Perceptron) In Weka, Classifiers-functions-MultilayerPerceptron

Decision Trees

J48 (Java implementation of C4.5) In Weka, classifiers-trees-J48

Support Vector Machines

SMO (sequential minimal optimization) for training SVM In Weka, classifiers-functions-SMO

Practice Scenario

Basic Comparing the performances of algorithms

MultilayerPerceptron vs. J48 vs. SVM Checking the trained model (structure & parameter)

Tuning parameters to get better models Understanding ‘Test options’ & ‘Classifier output’ in

Advanced Building committee machines using ‘meta’ algorithms for

classification Preprocessing / data manipulation – applying ‘Filter’ Batch experiment with ‘Experimenter’ Design & run a batch process with ‘KnowledgeFlow’

Dataset for Practice with Weka

Just open “iris.arff” in the data folder of Weka

Data format for Weka (.ARFF)

@RELATION iris@ATTRIBUTE sepallength REAL@ATTRIBUTE sepalwidth REAL@ATTRIBUTE petallength REAL@ATTRIBUTE petalwidth REAL@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}@DATA

5.1, 3.5, 1.4, 0.2, Iris-setosa4.9, 3.0, 1.4, 0.2, Iris-setosa4.7, 3.2, 1.3, 0.2, Iris-setosa…7.0, 3.2, 4.7, 1.4, Iris-versicolor6.4, 3.2, 4.5, 1.5, Iris-versicolor6.9, 3.1, 4.9, 1.5, Iris-versicolor…

Data (CSV format)

Header

Neural Networks in Weka

click • load a file that contains the training data by clicking ‘Open file’ button• ‘ARFF’ or ‘CSV’ formats are

readible

• Click ‘Classify’ tab• Click ‘Choose’ button• Select ‘weka – function - MultilayerPerceptron

• Click ‘MultilayerPerceptron’ • Set parameters for MLP• Set parameters for Test• Click ‘Start’ for learning

Some Notes on the Parameter Setting

Parameter Setting = Car Tuning need much experience or many times of trial you may get worse results if you are unlucky

Multilayer Perceptron (MLP) Main parameters for learning: hiddenLayers, learningRate,

momentum, trainingTime (epoch), seed

J48 Main parameters: unpruned, numFolds, minNumObj Many parameters are for controlling the size of the result tree,

i.e. confidenceFactor, pruning

SMO (SVM) Main parameters: c (complexity parameter), kernel, kernel

Test Options and Classifier Output

There are various metrics for evaluation

Setting the data set used for evaluation

How to Evaluate the Performance? (1/2)

Usually, build a ‘Confusion Matrix’ on the test data set

Evaluation Metrics Accuracy (percent correct) Precision Recall Many other metrics: F-measure, Kappa score,

For fare evaluation, the ‘cross-validation’ scheme is used

How to Evaluate the Performance? (2/2)

Confusion Matrix Real

Prediction Positive Negative

Positive TP FPAll with positive

Negative FN TNAll with

Negative Test

All with Disease

All without Disease

Everyone

FNTNFPTPTNTP

Accuracy

FNTPTP

RecallFPTP

Precision

As recall ↑ precision ↓conversely:

As recall ↓ precision ↑

Evaluation Method - Cross Validation

K-fold Cross Validation The data set is randomly divided into k

subsets. One of the k subsets is used as the ‘test set’

and the other k-1 subsets are put together to form a ‘training set’.

128 128128 128 128D1 D2 D3 D4 D5

128 128128 128 128D1 D2 D3 D4 D6

128 128128 128 128D2 D3 D4 D5 D6

iiError

kError

16-fold cross validation

Data Manipulation with Filter in Weka

Attribute Selection, discretize

Instance Re-sampling, selecting specified folds

Using Experimenter in Weka

Tool for ‘Batch’ experiments

• Set experiment type/iteration control• Set datasets / algorithms

Click ‘New’

• Select ‘Run’ tab and click ‘Start’• If it has finished successfully,

click ‘Analyse’ tab and see the summary

Usages of Experimenter

Model selection for classification/regression Various approaches

Repeated training/test set split Repeated cross-validation (c.f. double cross-validation) Averaging

Comparison between models / algorithms Paired t-test On various metrics: accuracies / RMSE / etc.

Batch and/or Distributed processing Load/save experiment settings http://weka.wikispaces.com/Remote+Experiment Multi-core support : utilize all the cores on a multi-core

KnowledgeFlow for Analysis Process Design

28(‘Process Flow Diagram’ of SAS® Enterprise Miner )

KnowledgeFlow: Example Usage

Decision tree (J48)

Simple CLI

Example command and result java weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M

0.2 -N 500 -V 0 -S 0 -E 20 -H a -t "C:\Program Files\Weka-3-6\data\iris.arff"

You may build a command line script for various experiments easily

Refer Ch.1 of WekaManual-3-*-*.pdf for further information

Other ML Open Source S/W’s

KNIME Konstanz Information Miner http://www.knime.org/

RapidMiner With Weka as its core http://rapid-i.com/index.php?lang=en

TANAGRA http://eric.univ-lyon2.fr/~ricco/tanagra/en/

General Information on Weka

Current version (2012-2-3) Stable version: 3.6.6 Developer version: 3.7.5

References

Weka Wiki: http://weka.wikispaces.com/ Primer: good starting point

Weka online documentation: http://www.cs.waikato.ac.nz/ml/weka/index_documentation.html

Textbook Ian H. Witten, Eibe Frank, Mark A. Hall, Data

Mining: Practical Machine Learning Tools and Techniques (Third Edition), Morgan Kaufmann, Jan. 2011.

Articles Data mining with WEKA, Part 1, Part 2, Part 3 in

IBM Technical Library Weka 를 이용한 예측프로그램 만들기 – 월간 마소 연재

(2009 7,8,9 월호 ) 블로그 , MS Live

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