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We introduce the first application of the lean methodology to machinelearning projects. Similar to lean startups and lean manufacturing, we arguethat lean machine learning (LeanML) can drastically slash avoidable wastes incommercial machine learning projects, reduce the business risk in investing inmachine learning capabilities and, in so doing, further democratize access tomachine learning. The lean design pattern we propose in this paper is based ontwo realizations. First, it is possible to estimate the best performance onemay achieve when predicting an outcome $y \in \mathcal{Y}$ using a given set ofexplanatory variables $x \in \mathcal{X}$, for a wide range of performancemetrics, and without training any predictive model. Second, doing so isconsiderably easier, faster, and cheaper than learning the best predictivemodel. We derive formulae expressing the best $R^2$, MSE, classificationaccuracy, and log-likelihood per observation achievable when using $x$ topredict $y$ as a function of the mutual information $I\left(y; x\right)$, andpossibly a measure of the variability of $y$ (e.g. its Shannon entropy in thecase of classification accuracy, and its variance in the case regression MSE).We illustrate the efficacy of the LeanML design pattern on a wide range ofregression and classification problems, synthetic and real-life.

LeanML: A Design Pattern To Slash Avoidable Wastes in Machine Learning Projects