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Machine Learning Meetup Notes: 2010-06-02
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== TODO == * Sampling: possible by student only, uses original data format, may need changes to work with current data. (Thomas) * Distribute dataset -- provide to at least Thomas * Superskills: Thomas & Mike attempts. * Naive Bayes vs LibSvm (in weka!) vs Neural networks vs Regression * Student classifier -- Java program to save/load models?! (can eliminate students not in test set!) * Format for future meetings * See how many students are new in the test set; * Create statistics/histograms for IQ/chance features -- for discretization for Naive Bayes * Add feature "IQ strength" & "chance strength"?
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