Unlocking AutoML’s Predictive Power: A Structural Sensitivity Analysis Approach

AutoML, or Automated Machine Learning, is a promising technology that makes it easier for non-experts to build machine learning models. However, there’s a trade-off between predictive power and interpretability of the model. Greater predictive performance often comes at the cost of simplified pipelines, which can be hard to understand.
The authors propose Amazon SageMaker Autopilot, a white-box AutoML solution that prioritizes both predictive power and pipeline simplicity. White-box refers to the ability to see inside the machine learning model and understand how it makes decisions.
SageMaker Autopilot uses Structural Sensitivity Analysis (SA), an optimization technique, to find the best balance between predictive performance and interpretability. SA helps identify the most important features in a dataset, which can be visualized using dimensionality reduction techniques like PCA or t-SNE.
The authors tested SageMaker Autopilot on 16 datasets and found that it could reduce inference time by up to 74.9% while maintaining predictive performance. The results were statistically significant, meaning the difference was unlikely due to chance.
In summary, Amazon SageMaker Autopilot is a powerful tool for building machine learning models without sacrificing interpretability. By prioritizing both predictive power and pipeline simplicity, it democratizes data science across fields, making it easier for non-experts to build accurate models without getting bogged down in complex technical details.