You work as a machine learning specialist for a medical imaging company. You and your machine learning team have been assigned the task of building a model that predicts whether a breast mass image indicates a benign or malignant tumor. Your model will be used to help physicians quickly decide how to treat their patients using a verified diagnosis. Which option gives the appropriate machine learning services and features to train your model for your image diagnosis problem?

Answer options:

A.Specify the SageMaker role arn used to give learning and hosting access to your data by using the role = sagemaker.get_role() statement in your jupyter notebook. Load your data into a pandas dataframe. Split your data into 80% training, 10% validation and 10% testing. Set the predictor_type hyperparameter to binary_classifier. Then run your training job using the sagemaker.create_training_job statement in your jupyter notebook.
B.Specify the SageMaker role arn used to give learning and hosting access to your data by using the role = sagemaker.get_execution_role() statement in your jupyter notebook. Load your data into a pandas dataframe. Split your data into 80% training, 10% validation and 10% testing. Set the predictor_type hyperparameter to binary_classifier. Then run your training job using the sagemaker.create_training_job statement in your jupyter notebook.
C.Specify the SageMaker role arn used to give learning and hosting access to your data by using the role = sagemaker.get_execution_role() statement in your jupyter notebook. Load your data into a pandas dataframe. Split your data into 80% training, 10% validation and 10% testing. Set the predictor_type hyperparameter to the regressor type. Then run your training job using the sagemaker.create_training_job statement in your jupyter notebook.
D.Specify the SageMaker role arn used to give learning and hosting access to your data by using the role = sagemaker.get_execution_role() statement in your jupyter notebook. Load your data into a pandas dataframe. Split your data into 80% training, 10% validation and 10% testing. Set the predictor_type hyperparameter to multiclass_classifier. Then run your training job using the sagemaker.create training_job statement in your jupyter notebook. 

Correct Answer: C
Option A is incorrect. You specify the SageMaker role arn used to give learning and hosting access to your data by using the role = sagemaker.get_execution_role() statement, not the role = sagemaker.get_role() statement. Also, since this problem is trying to predict whether an image mass is benign or malignant, you want to use a regression algorithm to give the probability that the mass is malignant, not a binary yes/no. Therefore, you should set the predictor_type hyperparameter to the value regressor, not binary_classifier.
Option B is incorrect. Since this problem is trying to predict whether an image mass is benign or malignant, you want to use a regression algorithm to give the probability that the mass is malignant, not a binary yes/no. Therefore, you should set the predictor_type hyperparameter to the value regressor, not binary_classifier.
Option C is correct. Since this problem is trying to predict whether an image mass is benign or malignant, you want to use a regression algorithm to give the probability that the mass is malignant, not a binary yes/no. Therefore, you should set the predictor_type hyperparameter to the regressor type. Also, it is correct to specify the SageMaker role arn used to give learning and hosting access to your data by using the role = sagemaker.get_execution_role() statement.
Option D is incorrect. Since this problem is trying to predict whether an image mass is benign or malignant, you want to use a regression algorithm to give the probability that the mass is malignant, not a classification across multiple classes. Therefore, you should set the predictor_type hyperparameter to a regressor, not a multiclass_classifier.
References:
Please see the AWS Amazon SageMaker Examples jupyter notebook titled Breast Cancer Prediction (https://github.com/aws/amazon-sagemaker-examples/blob/master/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast%20Cancer%20Prediction.ipynb),
The Amazon SageMaker page titled Linear Learner Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)