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