You work as a machine learning specialist for a consulting firm that has the NFL as a client. You are working on the passer completion probability model using statistics from in-play metrics. You are running your linear learner model in Amazon SageMaker using a CSV file representation of your passer completion probability statistics. You are now running your inference.
Some of the features and their data types are listed below.
|Feature Name |Data Type |
| Passer age |Numeric|
| Length of pass|Numeric|
| Complete (yes/no)|Categorical |
|Feature Name|Data Type |
| Distance between receiver and nearest defender|Numeric|
| Play called (post, crossing, screen, etc.)|Categorical |
You are using the Complete feature as your prediction response feature. You are now making predictions on new data. When you interrogate the response of your model, which of the following do you expect to find?

Answer options:

A.score: the prediction produced by the model
B.score: the prediction produced by the model AND predicted_class which is an integer from 0 to num_classes-1
C.score: single floating point number measuring the strength of the prediction AND predicted_label which is 0 or 1
D.score: the prediction produced by the model OR predicted_label which is 0 or 1

Answer: C
Option A is incorrect. For a binary classification (complete yes or no), the model produces a score denoting the strength of the prediction AND a predicted_label denoting complete or not complete.
Option B is incorrect. This option describes the response for multiclass classification, but you are working with binary classification.
Option C is correct. For a binary classification (complete yes or no), the model produces a score denoting the strength of the prediction AND a predicted_label denoting complete or not complete.
Option D is incorrect. For a binary classification (complete yes or no), the model produces a score denoting the strength of the prediction AND a predicted_label denoting complete or not complete.
Reference:
Please see the Amazon SageMaker developer guide titled Linear Learner Algorithm.