Rules • Use basic Python, numpy and matplotlib modules. Any other modules need my approval.
• Produce a LATEX-generated PDF of your report.
• Ask plenty of questions to ensure you have a good understanding of the project. • The code (and reports) should look vastly different for different groups. Very similar code will incur a hefty penalty.
• Everyone should participate…no excuses, no exceptions.
In this project we use pattern recognition to determine whether a subject is relaxed or planning. In this study EEG data was collected from patients in each of these two states. We build a classifier to help make automated decisions.
1. Study the dataset located here: https://archive.ics.uci.edu/ml/datasets/Planning+Relax. You will note that there are 182 training patterns of 2 classes with the last column being the class label.
2. Write code to produce produce the posterior probability P (C1|x)
3. Calculate the training error of your classifier, that is over the whole training set.
To ensure that our classifiers generalize, in practice we split the data into a training and a test set.
1. Split the data into 40%, 50%, 60%, 70%, 80%, and 90% training with the remainder being testing data.
2. Produce a table and a plot showing training error and testing error vs. percentage of training data.
In your report, take care to discuss the study and the 12 features that were collected from the subjects
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