python - Penalize error on one category svm sklearn? -


i've got ~100 data elements 1 class, , ~6000 class. when create svm using data, returns majority category label every new element try predict on. there way penalize misclassification of minority category sklearn svm, can use data elements , not have sample 100 elements majority category use in testing?

svc has following parameter can use

class_weight : {dict, 'auto'}, optional     set parameter c of class class_weight[i]*c     svc. if not given, classes supposed have     weight one. 'auto' mode uses values of y     automatically adjust weights inversely proportional     class frequencies.

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