Discriminative vs Generative learning

Instructor: Andrea Passerini

http://disi.unitn.it/~passerini/teaching/2013-2014/MachineLearning/Projects/7.pdf

Evaluate two simple learning algorithms, one generative and one discriminative, on a binary classification task.

Linear classifiers:

• Naive Bayes
• Perceptron

The “UCI Machine Learning Repository” is a collection of datasets commonly used to evaluate learning algorithms: http://archive.ics.uci.edu/ml/datasets.html

Choose two datasets with Categorical attributes and Binary classification task.

Chosen Datasets

• KRKPA7 [king rook vs king pawn (chess)]
• voting records (congress)

Task

• Implement Naive Bayes and Perceptron
• Run 10-fold cross validation on the two datasets
• Report cross validated accuracies of the two classifiers for each dataset