Diagnosing extrapolation: tree-based density estimation

There has historically been very little concern with extrapolation in Machine Learning, yet extrapolation can be critical to diagnose. Predictor functions are almost always learned on a set of highly correlated data comprising a very small segment of predictor space. Moreover, flexible predictors, by their very nature, are not controlled at points of extrapolation. This becomes a problem for diagnostic tools that require evaluation on a product distribution. It is also an issue when we are trying to optimize a response over some variable in the input space. Finally, it can be a problem in non-static systems in which the underlying predictor distribution gradually drifts with time or when typographical errors misrecord the values of some predictors. We present a diagnosis for extrapolation as a statistical test for a point originating from the data distribution as opposed to a null hypothesis uniform distribution. This allows us to employ general classification methods for estimating s...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Giles Hooker
• Keywords: Data Mining, Hypothesis Uniform Distribution, KDD 2004, Predictor Distribution, Predictor Functions