essors (FP): Learn parameters on the training set and apply
transformations to the training and test sets.</p></li>
<li><p>Classifiers (CL): Learn the relationship between experimental conditions
(i.e., "labels") and neural data on a training set, and then predict
experimental conditions from neural data in a test set.</p></li>
<li><p>Result metrics (RM): Aggregate results across validation splits and
over resampled runs and compute and plot final decoding accuracy metrics.</p></li>
<li><p>Cross-validators 