A company needs to develop a model that uses a machine learning (ML) model for...

To evaluate the contribution of each feature before model training, feature importance scores are a primary tool. In Amazon SageMaker Data Wrangler, a Quick Model can be used to rapidly generate a model on the dataset and return feature importance scores. These scores indicate how much each feature influences the target variable.

“Use Quick Model to generate feature importance scores quickly to help understand which features contribute the most to predictions. These scores range from 0 to 1, with higher values indicating higher contribution.”

This allows ML engineers to assess feature relevance without full-scale model development, thus saving time and effort in the feature selection phase of the pipeline.