Conditional Density Estimation with Class Probability Estimators

Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it. In this paper we compare three techniques for computing conditional density estimates using a class probability estimator, where this estimator is applied to the discretized target variable and used to derive instance weights for an underlying univariate density estimator; this yields a conditional density estimate. The three density estimators we compare are: a histogram estimator that has been used previously in this context, a normal density estimator, and a kernel estimator. In our experiments, the latter two deliver better performance, both in terms of cross-validated log-likelihood and in terms of quality of the resulting prediction intervals. The empirical coverage of the intervals is close to the desired confidence level in most case...

• Type: Conference
• Year: 2009
• Where: ACML
• Authors: Eibe Frank, Remco R. Bouckaert
• Keywords: ACML 2009, Conditional Density Estimate, Density Estimator, Machine Learning, Prediction Intervals