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Published online by Cambridge University Press: 23 March 2023
A good model aims to learn the underlying signal without overfitting (i.e. fitting to the noise in the data). This chapter has four main parts: The first part covers objective functions and errors. The second part covers various regularization techniques (weight penalty/decay, early stopping, ensemble, dropout, etc.) to prevent overfitting. The third part covers the Bayesian approach to model selection and model averaging. The fourth part covers the recent development of interpretable machine learning.
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