Question

A data science team at a real estate company needs to predict house sale prices from a tabular dataset containing 40 numeric and categorical features and about 120,000 rows. The relationships between features and price are highly non-linear, and the team wants strong predictive accuracy with minimal feature engineering while training efficiently on SageMaker. Which modeling approach should they choose?

Accepted Answer

Use the SageMaker XGBoost built-in algorithm configured for regression (reg:squarederror).

Answer

Use the SageMaker Linear Learner algorithm in regression mode.

Answer

Use the SageMaker K-Means algorithm to cluster homes and assign each cluster's mean price.

Answer

Use the SageMaker Random Cut Forest algorithm to score each record's price.