A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the…

Amazon SageMaker Data Wrangler is a visual interface specifically designed for data preparation and feature engineering, offering built-in analyses like Gini importance score analysis that can be performed with minimal or no code, making it the solution with the least development effort.

Performing principal component analysis (PCA) in a SageMaker notebook instance requires writing custom code and PCA is primarily a dimensionality reduction technique, not a direct method for generating feature importance scores in the way Gini importance does for individual features.

Performing singular value decomposition (SVD) analysis in a SageMaker notebook instance requires custom coding, and SVD is also a dimensionality reduction technique, not a direct method for assigning importance scores to original features.

While Lasso feature selection can identify important features by shrinking coefficients, it typically requires coding in a notebook instance, and 'multicollinearity feature' is a data characteristic to address, not a method for performing importance scoring with minimal effort.