# Classification with Naive Bayes classifier¶

Load a dataset using subject s01’s T-statistics for every run (‘glm_T_stats_perrun.nii.gz’) runs and the VT mask. Slice the datset using your sample attributes slicer so that there are only two categories: monkeys and mallards. Then slice the datasdet again into odd and even runs. Train and test a Naive bayes classifier (cosmo classify naive bayes) first training on the even-runs data and testing on the odds, then train on the odds and test on the evens.

Check your answers here: run classify naive bayes / Matlab output: run_classify_naive_bayes

What is the accuracy for monkey versus ladybug? Monkey versus lemur?

Do the accuracies change if you use betas (‘glm_betas_perrun.nii.gz’) instead of T-stats?

What if you use a different mask?