Machine Learning (ML) with Weka

Weka can classify data or approximate functions: choice of

many algorithms

Weka: can use GUI or command line

Click Explorer

Data Preprocessing

Open .arff file: click "Open file", browse to file

The well-known iris ML data set

For sepallength attribute we see distribution of classes (colors). We see min, max, mean, standard deviation of numeric attribute.

Data Preprocessing

Click Edit to view/modify the .arff file

Data Preprocessing

Click Filter Choose. Select filters > unsupervised > Instance > RandomizePress Apply. Click Edit: see that examples are now randomized

Data Preprocessing

Click Filter Choose. Select filters > unsupervised > Instance > NormalizePress Apply. Click Edit: see that examples are now normalized also. Can save

Data Preprocessing

Statistics have changed due to data normalization

Select Attributes if too many

Choose Principal Components (will select Ranker also) 0.517 petallength + 0.512 petalwidth - 0.492 sepalwidth - 0.478 sepallength-0.747 sepallength + 0.626 sepalwidth - 0.198 petallength + 0.104 petalwidthAlternative: Info gain attribute evaluation (will select Ranker also)

Classify

Click Classify, Choose: functions >Multilayer PerceptronLeft-click for properties, change GUI to true, press start to see ANN topology4 green input nodes(1 per attribute), 3 red hidden nodes {user controlled: defaults to a=(inputs +outputs) / 2}, 3 yellow output nodes (1 per class)

Data Preprocessing

Click Open file

Data Preprocessing

Click Open file

Data Preprocessing

Click Open file