  Algorithm performance in supervised learning is a combination of
memorization, generalization, and luck. By estimating how much information an
algorithm can memorize from a dataset, we can set a lower bound on the amount
of performance due to other factors such as generalization and luck. With this
goal in mind, we introduce the Labeling Distribution Matrix (LDM) as a tool for
estimating the capacity of learning algorithms. The method attempts to
characterize the diversity of possible outputs by an algorithm for different
training datasets, using this to measure algorithm flexibility and
responsiveness to data. We test the method on several supervised learning
algorithms, and find that while the results are not conclusive, the LDM does
allow us to gain potentially valuable insight into the prediction behavior of
algorithms. We also introduce the Label Recorder as an additional tool for
estimating algorithm capacity, with more promising initial results.
