Machine Learning: End-to-End guide for Java developers

Building a classifier

Once sensor samples are represented as feature vectors having the class assigned, it is possible to apply standard techniques for supervised classification, including feature selection, feature discretization, model learning, k-fold cross validation, and so on. The chapter will not delve into the details of the machine learning algorithms. Any algorithm that supports numerical features can be applied, including SVMs, random forest, AdaBoost, decision trees, neural networks, multi-layer perceptrons, and others.

Therefore, let's start with a basic one, decision trees: load the dataset, build set class attribute, build a decision tree model, and output the model:

String databasePath = "/Users/bostjan/Dropbox/ML Java Book/book/datasets/chap9/features.arff"; ...