Adding Data – Multi-label

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.

// String data
sendToIndico('/custom/add_data', [["string", ["label1", "label2"]]])

// Image data
sendToIndico('/custom/add_data', [["image-url or b64-image", ["label1", "label2"]]])

// Batch add data
sendToIndico('/custom/batch/add_data', [["string1", ["label1", "label2"]], ["string2", ["label2"]]])

// Specifying domain
sendToIndico('/custom/batch/add_data?domain=topics', [["string1", ["label1", "label2"]], ["string2", ["label2"]]])