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We consider nonparametric prediction with multiple covariates, in particularcategorical or functional predictors, or a mixture of both. The method proposedbases on an extension of the Nadaraya-Watson estimator where a kernel functionis applied on a linear combination of distance measures each calculated onsingle covariates, with weights being estimated from the training data. Thedependent variable can be categorical (binary or multi-class) or continuous,thus we consider both classification and regression problems. The methodologypresented is illustrated and evaluated on artificial and real world data.Particularly it is observed that prediction accuracy can be increased, andirrelevant, noise variables can be identified/removed by `downgrading' thecorresponding distance measures in a completely data-driven way.

Nonparametric Regression and Classification with Functional, Categorical, and Mixed Covariates