Notice – All publicly available Indico APIs will be deprecated on Jan 1, 2020
Use this model type when any number of the supplied labels can apply to each example (i.e. each label is independent from the others). This is equivalent to training a separate classifier to predict whether or not each possible label applies to a provided document. The probabilities returned by a call to `predict()` can each range from 0. to 1. and are not required to sum to 1. Examples of multi-label classification include predicting the topics an article covers or predicting what themes a user mentioned in a review.
In this case you can supply multiple labels per data point when adding data to your custom collection.