Which solution will meet this requirement with the LEAST development effort?
Use SageMaker Data Wrangler to perform a Gini importance score analysis.
Use a SageMaker notebook instance to perform principal component analysis (PCA).
Use a SageMaker notebook instance to perform a singular value decomposition analysis.
Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.
Explanations:
SageMaker Data Wrangler provides a built-in tool to analyze feature importance, including Gini importance scores. It is a user-friendly option with minimal development effort for this task.
Principal Component Analysis (PCA) is a dimensionality reduction technique, not specifically used for obtaining feature importance scores. PCA transforms features rather than providing importance scores.
Singular Value Decomposition (SVD) is also a dimensionality reduction technique, similar to PCA. It is not designed for generating feature importance scores.
Lasso regression (using L1 regularization) can perform feature selection, but it is not a feature of SageMaker’s multicollinearity tool. Additionally, using a SageMaker notebook instance for this requires more custom coding than SageMaker Data Wrangler.
From what I’ve seen, the answer is:
Use SageMaker Data Wrangler to perform a Gini importance score analysis.