Exam AWS Certified Machine Learning - Specialty topic 1 question 367 discussion

C. 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. Data Wrangler's quick model visualization provides a fast way to get insights into feature importance. This option has the least operational overhead, as it requires minimal setup and coding.

A. No, un modelo lineal requiere calificar manualmente coeficientes y puede no captar relaciones complejas. B. No, random forest da importancia Gini pero implica más configuración y ajuste. C. No, la visualización rápida en Data Wrangler es útil pero no automatiza la selección de 2000 atributos. D. Sí, Autopilot con un informe de Clarify automatiza el entrenamiento y extrae la importancia de las características con mínimo esfuerzo.

This approach leverages the automation capabilities of SageMaker Autopilot and the detailed analysis provided by SageMaker Clarify, ensuring an efficient and effective solution for identifying the most valuable attributes for predicting stock returns.