A data scientist uses SAS DLPy to train a CNN on an image dataset stored in CAS. After training, they want to apply the model to score new images. Which method is used in SAS DLPy to score new data?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the SAS DLPy Model class provides a predict() method that applies the trained model to a new image table in CAS and returns predicted labels and probabilities. A (model.fit()) is used for training, not scoring.
Full explanation below image
Full Explanation
B is correct because the SAS DLPy Model class provides a predict() method that applies the trained model to a new image table in CAS and returns predicted labels and probabilities. A (model.fit()) is used for training, not scoring. C (model.score()) exists in DLPy but is used to compute accuracy metrics, not to return predictions on new data. D names a valid underlying CAS action (deepLearn.dlScore) but the DLPy API exposes this through predict(), not direct CAS action calls.