You are currently offline. Some features of the site may not work correctly.

Corpus ID: 59336361

Testing Conditional Predictive Independence in Supervised Learning Algorithms

@article{Watson2019TestingCP,
title={Testing Conditional Predictive Independence in Supervised Learning Algorithms},
author={David S. Watson and Marvin N. Wright},
journal={ArXiv},
year={2019},
volume={abs/1901.09917}
}

We propose a general test of conditional independence. The conditional predictive impact (CPI) is a provably consistent and unbiased estimator of one or several features’ association with a given outcome, conditional on a (potentially empty) reduced feature set. The measure can be calculated using any supervised learning algorithm and loss function. It relies on no parametric assumptions and applies equally well to continuous and categorical predictors and outcomes. The CPI can be efficiently… CONTINUE READING