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习题10-2 #71

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simo-an opened this issue Jan 25, 2022 · 0 comments

simo-an commented Jan 25, 2022

习题10-2 集成学习是否可以避免过拟合？

解答

什么是过拟合？
过拟合是指模型对于训练数据拟合呈现过当的情况，反映到评估指标上就是模型在训练集上的表现很好，但是在测试集上的表现较差。结果就是训练出的模型泛化能力差。
集成学习是否可以避免过拟合？
集成学习是把多个模型集成到一起来作为共同的模型，可以降低单一模型的过拟合风险。
同时我们期望每个弱模型的差异要尽量大一些。

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