model selection

通过使用可用数据训练多种模型并比较其在独立数据集上的表现,本文介绍了如何避免过拟合并选择具有最佳预测性能的模型。

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       We have already seen that, in the maximum likelihood approach, the performance on the training set is not a good indicator of predictive performance on un-seen data due to the problem of over-fitting. If data is plentiful, then one approach is simply to use some of the available data to train a range of models, or a given model with a range of values for its complexity parameters, and then to compare them on independent data, sometimes called a validation set,and select the one having the best predictive performance.
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