We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available. We introduce texttt{KernelCobra}, a non-linear learning strategy for combining an arbitrary number of initial predictors. texttt{KernelCobra} builds on the COBRA algorithm introduced by citet{biau2016cobra}, which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalize this idea by using a kernel to better encapsulate the proximity information. Such a smoothing kernel provides more representative weights to each of the training points which are used to build the aggregate and final predictor, and texttt{KernelCobra} systematically outperforms the COBRA algorithm. While COBRA is intended for regression, texttt{KernelCobra} deals with classification and regression. texttt{KernelCobra} is included as part of the open source Python package texttt{Pycobra} (0.2.4 and onward), introduced by citet{guedj2018pycobra}. Numerical experiments assess the performance (in terms of pure prediction and computational complexity) of texttt{KernelCobra} on real-life and synthetic datasets.
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