turicreate.activity_classifier.ActivityClassifier.classify¶

ActivityClassifier.classify(dataset, output_frequency='per_row')¶

Return a classification, for each prediction_window examples in the dataset, using the trained activity classification 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 and session id used for model training, but does not require a target column. Additional columns are ignored.

output_frequency : {‘per_row’, ‘per_window’}, optional

The frequency of the predictions which is one of:

‘per_row’: Each prediction is returned prediction_window times.
‘per_window’: Return a single prediction for each prediction_window rows in dataset per session_id.

Returns:

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