You uploaded sales data into SageMaker Studio and need feature importance scores to guide feature engineering with minimal development effort. Which option provides importance scores with the least work?

SageMaker Data Wrangler offers built-in transformations, including feature importance calculations like Gini importance, directly within its visual interface. This provides a low-code solution for obtaining feature importance scores with minimal development effort, aligning with the "least work" requirement. Running PCA or SVD from a SageMaker notebook instance would require writing and executing code, which is more development effort than using Data Wrangler's visual interface. While PCA and SVD can reduce dimensionality, they don't directly provide feature importance scores in the same way Gini importance does. Multicollinearity analysis and LASSO-based feature selection also involve more coding and setup than Data Wrangler's integrated feature importance calculation.