# psychopy_ext.fmri.Analysis.svm

`Analysis.``svm`(evds, nIter=100, clf=LinearNuSVMC(svm_impl='NU_SVC', kernel=LinearLSKernel(), weight=, []weight_label=[]))

  Runs a support vector machine pairwise.

  Process:
    - Normalize data by subtracting the mean across voxels per chunk per condition (target).
    - Split data into a training set (about 75% of all values) and a testing set (about 25% of values), unless there are only two runs, in which case is 50% training and 50% testing.
    - For each pair of conditions, train the classifier.
    - Then test on the average of the testing set, i.e., only on two samples. This trick usually boosts the performance (credit: Hans P. Op de Beeck)

  Args: | evds (event-related mvpa dataset)
  Kwargs: | nIter (int, default: 100) Number of random splits into a training and testing sets. clf (mvpa classfier, default: Linear Nu SVM)
  Returns: | A header and a results matrix with four columns: iter: iteration number stim1.cond: first condition stim2.cond: second condition subj_resp: one minus the correlation value