  The success of machine learning on a given task dependson, among other
things, which learning algorithm is selected and its associated
hyperparameters. Selecting an appropriate learning algorithm and setting its
hyperparameters for a given data set can be a challenging task, especially for
users who are not experts in machine learning. Previous work has examined using
meta-features to predict which learning algorithm and hyperparameters should be
used. However, choosing a set of meta-features that are predictive of algorithm
performance is difficult. Here, we propose to apply collaborative filtering
techniques to learning algorithm and hyperparameter selection, and find that
doing so avoids determining which meta-features to use and outperforms
traditional meta-learning approaches in many cases.
