Abstract The scientific needs and computational limitations of the twentieth century fashioned classical statistical methodology. Both the needs and limitations have changed in the twenty-first, and so has the methodology. Large-scale prediction algorithms—neural nets, deep learning, boosting, support vector machines, random forests—have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic datasets. How do these algorithms compare with standard regression techniques such as ordinary least squares or logistic regression? Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (significance testing). Most of the discussion is carried out through small numerical examples.

中文翻译：

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预测、估计和归因

摘要 20 世纪的科学需要和计算限制塑造了经典的统计方法。需求和限制在二十一世纪都发生了变化，方法论也发生了变化。大规模预测算法——神经网络、深度学习、提升、支持向量机、随机森林——已经在大众媒体上获得了明星地位。它们被认为是回归传统的继承者，但它们是在巨大的规模和巨大的数据集上进行的。这些算法与标准回归技术（例如普通最小二乘法或逻辑回归）相比如何？将检查几个关键差异，重点是预测与估计或预测与归因（显着性检验）之间的差异。