You work as a machine learning specialist for a bank. Your bank management team is concerned about a recent increase in fraudulent transactions. You need to build a machine learning model to recognize fraudulent transactions in real-time. The following is a sample of the dataset you are using to train your model:
| Transaction ID |Account ID|type|amount |source | … |fraud |
|12576477 |37564378|debit |350.00|ATM | … |not_fraud |
|39844569 |74897544|credit|756.23|ATM | … |not_fraud |
|54986984 |55656753|credit|243.90|ATM | … |undetermined|
|34567863 |27564378|debit |1250.00|ATM | … |fraud|
You are using the fraudulent feature as your label. You have decided to use the linear learner built-in algorithm for your model. Which predictor type should you use for your linear learner?

Answer options:

A.binary_classifier
B.regressor
C.cross_entropy_loss
D.multiclass_classifier

Answer: D
Option A is incorrect. The binary_classifier predictor type is used when your target feature is binary, either yes or no, 1 or 0, etc. Your target feature has three potential values. Therefore it is classified across multiple classes.
Option B is incorrect. The regressor predictor type is used for regression models where your target feature is a continuous numeric value. Your target feature has three potential values. Therefore it is classified across multiple classes.
Option C is incorrect. The cross_entropy_loss is an option for the binary_classifier_model_selection_criteria parameter of the linear learner SageMaker algorithm. This parameter is used when you are using a binary classifier as your predictor type.
Option D is correct. The multiclass_classifier predictor type is used when your target feature has more than two potential values. Your target feature has three potential values. Therefore it is classified across multiple classes.
Reference:
Please see the Amazon SageMaker developer guide titled Linear Learner Algorithm, the Amazon SageMaker read the docs API doc titled LinearLearner, and the Amazon Machine Learning developer guide titled Multiclass Classification.