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?
Click on the arrows to vote for the correct answer
A. B. C. D.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.
Based on the given scenario, a linear learner model is being used in Amazon SageMaker to predict the completion probability of a passer in NFL games using various features such as passer age, length of pass, distance between receiver and nearest defender, and play called. The response feature used for prediction is whether the pass was complete or not, represented as a categorical variable (yes/no).
When running inference on new data, the response of the model will contain information about the prediction produced by the model. Specifically, it is expected that the response will include a score, which is a single floating-point number measuring the strength of the prediction.
Option A suggests that the response will only contain the score, which is correct. Therefore, Option A is a possible correct answer.
Option B suggests that the response will contain both the score and predicted_class, which is an integer value representing the class of the prediction (0 to num_classes-1). However, this is not applicable in this scenario as the prediction response feature is categorical with two possible values (yes/no). Therefore, Option B is not a correct answer.
Option C suggests that the response will contain the score and predicted_label, which is a binary value (0 or 1) representing the predicted class label. This option is a possible correct answer because the prediction response feature is categorical with two possible values, and the predicted_label will represent which category the prediction falls into (yes or no).
Option D suggests that the response will contain either the score or predicted_label, which is not accurate because both the score and predicted_label are necessary to fully understand the prediction results. Therefore, Option D is not a correct answer.
In conclusion, based on the scenario provided, the prediction response from the linear learner model in Amazon SageMaker is expected to include the score and predicted_label, as mentioned in Option C.