  We introduce a simple, interpretable strategy for making predictions on test
data when the features of the test data are available at the time of model
fitting. Our proposal - customized training - clusters the data to find
training points close to each test point and then fits an $\ell_1$-regularized
model (lasso) separately in each training cluster. This approach combines the
local adaptivity of $k$-nearest neighbors with the interpretability of the
lasso. Although we use the lasso for the model fitting, any supervised learning
method can be applied to the customized training sets. We apply the method to a
mass-spectrometric imaging data set from an ongoing collaboration in gastric
cancer detection which demonstrates the power and interpretability of the
technique. Our idea is simple but potentially useful in situations where the
data have some underlying structure.
