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The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.

机器学习算法及其性能的计算分析是'''理论计算机科学 Theoretical computer science'''的一个分支，也被称为'''机器学习理论 Computational learning theory'''。由于训练集是有限的，未来是不确定的，学习理论通常不能保证算法的性能。相反，性能的概率界限是相当常见的。'''偏差-方差分解 Bias–variance decomposition'''就是量化泛化'''误差 Errors and residuals'''的一种方法。

机器学习算法及其性能的计算分析是'''理论计算机科学 Theoretical Computer Science'''的一个分支，也被称为'''机器学习理论 Computational Learning Theory'''。由于训练集是有限的，未来是不确定的，学习理论通常不能保证算法的性能。相反，性能的概率界限是相当常见的。'''偏差-方差分解 Bias–variance Decomposition'''就是量化泛化'''误差 Errors and Residuals'''的一种方法。

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   --[[用户:趣木木|趣木木]]（[[用户讨论:趣木木|讨论]]）专有名词记得大写 通篇检查一下下