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 > Randomize Press Apply. Click Edit: see that examples are now randomized

Data Preprocessing Click Filter Choose. Select filters > unsupervised > Instance > Normalize Press 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) petallength petalwidth sepalwidth sepallength sepallength sepalwidth petallength petalwidth Alternative: Info gain attribute evaluation (will select Ranker also)

Classify Click Classify, Choose: functions > Multilayer Perceptron Left-click for properties, change GUI to true, press start to see ANN topology 4 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