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.
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?