Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)

Understand

Supervised Machine Learning (SML) algorithms such as Gradient Boosting, Random Forest, and Neural Networks have become popular in recent years due to their increased predictive performance over traditional statistical methods.

• This is especially true with large data sets (millions or more observations and hundreds to thousands of predictors).
• However, the complexity of the SML models makes them opaque and hard to interpret without additional tools.
• There has been a lot of interest recently in developing global and local diagnostics for interpreting and explaining SML models.