turicreate.text_classifier.TextClassifier.classify

TextClassifier.classify(dataset)

Return a classification, for each example in the dataset, using the trained model. The output SFrame contains predictions as both class labels as well as probabilities that the predicted value is the associated label.

Parameters:

dataset : SFrame

dataset of new observations. Must include columns with the same names as the features used for model training, but does not require a target column. Additional columns are ignored.

Returns:

out : SFrame

An SFrame with model predictions i.e class labels and probabilities.

See also

create, evaluate, predict

Examples

>>> import turicreate as tc
>>> dataset = tc.SFrame({'rating': [1, 5], 'text': ['hate it', 'love it']})
>>> m = tc.text_classifier.create(dataset, 'rating', features=['text'])
>>> output = m.classify(dataset)