X-FIPE: eXtended Feature Impact for Prediction Explanation
- 데이터 사이언스, 머신 러닝 및 MLOps
- 의료 서비스 및 생명 공학
- Moscone South | Level 3 | 314
- 35 min
Many enterprises have built their own machine learning platforms in the cloud using Databricks, e.g. Humana FlorenceAI. In order to effectively drive the adoption of predictive models in daily business operations, data scientists and business teams need to work closely to make sure they serve the consumer needs in compliance with regulatory rules. Model interpretability is key. In this talk, we would like to share an explainable AI algorithm developed at Humana, X-FIPE, eXtended Feature Impact for Prediction Explanation.
X-FIPE is a top-driver algorithm to calculate feature importance for any machine learning predictive models, whether it is Python or PySpark, at a local level. Instead of showing the feature importance on a population level, it can find the top drivers for each observation or member. These top drivers could differ widely from one member to another member in the population. it not only helps explain the predictive model, but also offer users actionable insights.
Compared with widely used algorithms, e.g. LIME, SHAP, and FIPE, X-FIPE improves the time complexity from linear O(n) to logarithmic O(log(n)), where n is the number of used model features. Also, we discovered the connection between X-FIPE value and Shapley value -- X-FIPE a first order approximation of Shapley value. Our observation shows that the most contribution of Shapley value of a feature comes from the marginal contribution when it is first added and when it is last removed from the full features. This is why the X-FIPE keeps enough accuracy and also reduces the computation.
Hopefully this talk will provide you a path forward to include explainable AI into your machine learning workflows, you are encouraged to try out and contribute to our open source Python package xfipe soon to come.