Points of Significance: Model selection and overfitting

By Jake Lever, Martin Krzywinski, Naomi Altman. Nature Methods, 2016

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Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, methodology): “, 7], in general, the trained model should exhibit robust performance on test data using relevant properties of the trained data. For more detailed description of overfitting and model selection, see [43]. Preventing overfitting also requires a very careful design of the model evaluation scheme, inc”
Machine Learning Methods in Drug Discovery (2020, background): “In contrast, underfitting models fail to recognize the data sets’ underlying trend and generalize the new data inputted [62].”
Filter-enhanced MLP is All You Need for Sequential Recommendation (2022, background): “It has been found that deep neural networks tend to overfit on noisy data [7, 24].”
Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, background): “However, even such learning algorithms tend to overfit training data due to their model complexity [18].”