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overfitting

Why the simplest explanation is always the best

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PreambleThe simplest explanation is always the best among the explanations that are representative.  It is called the principle of parsimony,  Occam's razor. It is the bedrock of scientific enlightenment. There is a recent development that people start to do a category error to discard this...

machine learning data science overfitting complexity complexity theory interpretable machine learning bayesian statistics. statistical models evalutionary computation overgenearlisation

Overfitting is about complexity ranking of inductive biases : Algorithmic recipe

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Preamble    Figure: Moon patterns human brain invents. (Wikipedia)Detecting overfitting is inherently a comparison problem of the complexity of multiple objects, i.e., models or an algorithm capable of making predictions. A model is overfitted (underfitted) if we only compare it to another model....

physics machine learning deep learning overfitting statistics interpretable machine learning sparse recovery statistical mechanics statistical models inductive bias

Empirical risk minimization is not learning : A mathematical definition of learning and re-understanding of overfitting and Occam's razor in machine learning

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Simionescu Function (Wikipedia)PreambleThe holy grail of machine learning appears to be the empirical risk minimisation. However, on the contrary to general dogma,  the primary objective of machine learning is not risk minimisation per se but mimicking human or animal learning. Empirical risk...

risk machine learning deep learning supervised learning overfitting occam representation learning optimization empirical risk minimisation

Core principles of sustainable data science, machine learning and AI product development: Research as a core driver

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Kindly reposto to KDnuggets  by Gregory Piatetsky-Shapiro Preamble  Almost all businesses and industry embraced Machine learning (ML) technologies. Apart from ROI concerns, as it is an expensive endeavour to develop and deploy a service driven by ML techniques, sustainability as in going beyond...

ai machine learning data science statistical learning l1 supervised learning linear algebra overtraining overfitting statistics principles. sustainable development

Understanding overfitting: an inaccurate meme in supervised learning

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Kindly reposted to KDnuggets by Gregory Piatetsky-Shapiro with the title Understanding overfitting: an inaccurate meme in machine learning Preamble There is a lot of confusion among practitioners regarding the concept of overfitting. It seems like, a kind of an urban legend or a meme, a folklore...

machine learning data science prediction statistical learning supervised learning gnu r cross-validation overtraining r programming overfitting regression statistics r r project regularisation