LeanML：一种在机器学习项目中减少浪费的设计模式
LeanML: A Design Pattern To Slash Avoidable Wastes in Machine Learning Projects
摘要：
我们介绍了精益方法在机器学习项目中的首次应用。与精益创业和精益制造类似，我们认为精益机器学习（LeanML）可以大幅削减商业机器学习项目中可避免的浪费，降低投资机器学习能力的商业风险，并在这样做的过程中，进一步民主化机器学习。我们在本文中提出的精益设计模式是基于两个实现。首先，对于广泛的性能度量，可以估计当使用给定的解释变量集$x \in \mathcal{X}$来预测结果$y \in \mathcal{Y}$时可以实现的最佳性能，而无需训练任何预测模型。其次，这样做比学习最好的预测模型更容易、更快、更便宜。我们推导公式表达最好的$R^2$，MSE，分类精度，和对数似然每个观察可实现时，使用$x$预测$y$作为互信息的函数$I\left(y; x\right)$，并可能测量的变化$y$（例如，其香农熵的情况下的分类精度，其方差的情况下回归MSE）。我们说明了LeanML设计模式在广泛的回归和分类问题上的功效，合成和现实生活。
英文摘要：
We introduce the first application of the lean methodology to machine learning projects. Similar to lean startups and lean manufacturing, we argue that lean machine learning (LeanML) can drastically slash avoidable wastes in commercial machine learning projects, reduce the business risk in investing in machine learning capabilities and, in so doing, further democratize access to machine learning. The lean design pattern we propose in this paper is based on two realizations. First, it is possible to estimate the best performance one may achieve when predicting an outcome $y \in \mathcal{Y}$ using a given set of explanatory variables $x \in \mathcal{X}$, for a wide range of performance metrics, and without training any predictive model. Second, doing so is considerably easier, faster, and cheaper than learning the best predictive model. We derive formulae expressing the best $R^2$, MSE, classification accuracy, and log-likelihood per observation achievable when using $x$ to predict $y$ as a function of the mutual information $I\left(y; x\right)$, and possibly a measure of the variability of $y$ (e.g. its Shannon entropy in the case of classification accuracy, and its variance in the case regression MSE). We illustrate the efficacy of the LeanML design pattern on a wide range of regression and classification problems, synthetic and real-life.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
