Which solution will meet these requirements with the LEAST operational overhead?
Use the linear leaner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.
Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.
Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. Identify the most predictive features based on the quick mode’s feature importance scores.
Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.
Explanations:
While linear regression can be used, it requires manual tuning of feature selection and does not provide the most streamlined solution. SageMaker’s linear learner doesn’t automatically rank features in a way that minimizes operational overhead.
Random forests can identify feature importance, but using them for regression requires more effort in model tuning and validation compared to an automated solution. This leads to higher operational overhead.
SageMaker Data Wrangler can visualize data and provide insights, but it doesn’t automatically generate models or streamline feature importance discovery the way a more automated tool like Autopilot would.
Amazon SageMaker Autopilot automates the machine learning pipeline, including model selection, hyperparameter tuning, and feature importance identification, providing the least operational overhead for this task.
I have a feeling that the answer is:
Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.