You work as a machine learning specialist for a real estate company. Your company wishes to have you develop a model that predicts if a given property is in a “high value” neighborhood (properties with a median household value at or above $180,000). Your real estate agents will use this model to prioritize their sales work based on potential commission for any given property in their list of potential sales leads. Which option is the best approach to solve this problem?

Answer options:

A.Use SageMaker Linear Learner optimizing for a continuous objective, such as mean square error, cross-entropy loss, or absolute error to predict the median household value for each district.
B.Use SageMaker Linear Learner optimizing for a discrete objective suited for classification, such as F1, precision, recall, or accuracy to predict whether or not a district is "high value".
C.Use SageMaker Linear Learner optimizing for a continuous objective, such as mean square error, cross-entropy loss, or absolute error to predict whether or not a district is "high value".
D.Use SageMaker Linear Learner optimizing for a continuous objective, such as F1, precision, recall, or accuracy to predict the median household value for each district.

Correct Answer: B
Option A is incorrect. We are solving a classification problem: predict if a given property is in a “high value” neighborhood. This is a discrete objective, which is suited for a classification solution, not a regression solution. The mean square error, cross-entropy loss, and absolute error objectives are used for regression problems.
Option B is correct. We are solving a classification problem: predict if a given property is in a “high value” neighborhood. Therefore, we will want to optimize using discrete objects such as F1, precision, recall, or accuracy.
Option C is incorrect. This option describes using continuous objectives (mean square error, cross-entropy loss, or absolute error) to solve a classification problem: predict whether or not a district is "high value."
Option D is incorrect. This option describes using discrete objectives (F1, precision, recall, or accuracy) to solve a regression problem: predict the median household value for each district.
References:
Please see the Kaggle challenge titled Ethical ML: California Housing Classification (https://www.kaggle.com/c/ethicalml-cahousing/overview),
The Amazon SageMaker developer guide titled Linear Learner Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)