2025年机器学习基础维基翻译超参数选择 K近邻法及简单的sklearn例子

科技前沿 • 2025-02-19 22:28 • 阅读 53

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In the context of mechine learning, hyperparameter optimization or model
selection is the problem of choosing a set of hyperparameters for a
learning algorithm, usually with the goal of optimizing a measure of
the algorithm‘s performance on a independent data set.
超参数的选择是在独立数据集上算法表现最优。
Often cross-validation is used to estimate the generalization performance.
一般使用交叉验证。
Hyerparameter optimization contrasts with actual learning problems, which
are also often cast as optimization problems,but optimize a loss function
on the training set alone. In effect, learning algorithms learn parameters
that model/reconstruct their inputs well, while hyperparameter optimiztion
is to ensure the model dose not overfit its data by tuning.
算法的目标是更好地拟合数据，超参数最优化是防止过拟合。
e.g. regularization:
这里的正则化是指如AIC BIC引入的惩罚项。
正则化防止过拟合的道理，是对参数加限定，使其不会“拟合”得那么好，去掉一些
对噪声的过分拟合。

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2025年机器学习基础 维基翻译 超参数选择 K近邻法 及简单的sklearn例子

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2025年机器学习基础维基翻译超参数选择 K近邻法及简单的sklearn例子