S7_WEKAIntro

7/29/2019 S7_WEKAIntro

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S7:IntrotoWekaLab

ShawndraHill

Spring2013

TR1:30-3pmand3-4:30


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Preprocessing

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Preprocessing:supervisedsampling

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Unbalanced class,supervised sampling can be

used to balance the data

set.

Click Choose in the filter

section, and follow this

path:


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Preprocessing:supervisedsampling

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Click on theresample box

next to the

Choose button,

and the pop-up

window

emerges to setthe desired

parameters

A balanced

sample is now

obtained


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Preprocessing:unsupervisedsampling

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Preprocessing:filters

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For a numeric transformation filter, click Choose, and then follow the path shown

below, selecting NumericTransform. The pop-up to set up the transformation emerge

by clicking the NumericTransform box next to the Choose button. To go-back from the

transformation, press the button Undo in the preprocessing menu.


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Preprocessing:filters

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Featureselecon

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Go to the Select attributes section to perform feature

selection. You need to define 2 things in order to do so: what

attribute evaluator and what search method to use.

Click on the Choose

buttons to access and

set the options

A"ribute

evaluatorop/ons:

Searchmethod

op/ons:


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Featureselecon:(InfoGain,Ranker)

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Click on the Choosebuttons to access and

set the options


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Featureselecon:(PCA,Ranker)

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Featureselecon:(Wrapper,GreedySTW)

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Featureselecon:modelswith

selectedaQributes

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Removeunselected

a"ributesintheTRAINING

set;savethemodified

TRAININGfile

ThenopentheTESTsetfile,andremoveunselecteda"ributesinthesameway.Save

themodifiedTRAININGandTESTsetfiles,opentheminWEKAandrunyourmodel


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Classificaon:Testsetopons


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Classificaon:K-NN

To create a K-NN classifier, go to the classifier tab and select options as indicated

above. Once you select Ibk, you can click in the Ibk box (right next to the Choose

button) and set the parameters in the pop-up window (figure in the right)


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Classificaon:NaveBayes


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Classificaon:Decisiontrees


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Numericpredicon:Linearregression


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Numericpredicon:NeuralNetworks


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Numericpredicon:NeuralNetworks

Clickingin More youcanlearn

abouttheNNparameters.Wekadescrip/onsforsomeofthe

importantparametersareshown

below:


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Classificaon:Output


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Classificaon:Output

Correctlyclassifiedinstances:52699639=54.68%

TPrate:Class1:2056(2056+2770)=0.427

Class2:3213(3213+1957)=0.668

ThediagonalsofTPandFPrate

sumupto1


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Classificaon:Output


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Classificaon:Output

Predicted

class

Class

probabilityes/mate

NOTES:

1:1 class 1, label 1

2:0 class 2, label 0

Class labels can beanything

First (second)

column in class

probability estimates

indicate probability

of being class 1 (class

2). E.g., the predicted

prob. of obs.1 being

class 1 is 0.578

+ indicates mistakes

in the classification

* Indicates the

predicted class


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Classificaon:Output

Predicted

class

Class

probability

es/mate

Class probability estimates can be stored using

the command line, an example below.

Get to the command line in Windows by typing

cmd in the Run dialogue box.

copy your training and test .arff files to the Weka directory, and

then use the following command line:

java -cp weka.jar weka.classifiers.trees.J48 -t TRAIN.arff -T

TEST.arff -p 0 >filename.probs


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Classificaon:ROCcurves


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Visualizaontab


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Visualizaontab


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S7:IntrotoWekaLab

ShawndraHill

Spring2013

TR1:30-3pmand3-4:30

S7_WEKAIntro

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