Setting a threshold in classifier output in Python

As far as I know, SVC itself does not allow thresholding of probabilities in the manner you want. You can do a second pass of indexing and get the accepted labels after you build labels_predicted and probabilities.

thresh = 0.9
accepted_probabilities_idx = probabilities.max(axis=1) > thresh
accepted_labels_predicted = labels_predicted[accepted_probabilities_idx]
accepted_new_data = pandas.DataFrame(new_data, index=accepted_probabilities_idx)

I am not sure what you want to do with data where ML-probability with low. This solution discards it completely.