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

The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity