turicreate.sound_classifier.SoundClassifier.classify

SoundClassifier.classify(dataset, verbose=True, batch_size=64)

Return the classification for each examples in the dataset. The output SFrame contains predicted class labels and its probability.

Parameters:

dataset : SFrame | SArray | dict

The audio data to be classified. If dataset is an SFrame, it must have a column with the same name as the feature used for model training, but does not require a target column. Additional columns are ignored.

verbose : bool, optional

If True, prints progress updates and model details.

batch_size : int, optional

If you are getting memory errors, try decreasing this value. If you have a powerful computer, increasing this value may improve performance.

Returns:

out : SFrame

An SFrame with model predictions, both class labels and probabilities.

See also

create, evaluate, predict

Examples

>>> classes = model.classify(data)