In
statistics and
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
, double descent is the phenomenon where a
statistical model with a small number of
parameter
A parameter (), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when ...
s and a model with an extremely large number of parameters have a small error, but a model whose number of parameters is about the same as the number of
data points
In statistics, a unit of observation is the unit described by the data that one analyzes. A study may treat groups as a unit of observation with a country as the unit of analysis, drawing conclusions on group characteristics from data collected at ...
used to train the model will have a large error.
This phenomenon seems to contradict the
bias-variance tradeoff in classical statistics, which states that having too many parameters will yield an extremely large error.
See also
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Bias–variance tradeoff
In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
The bias–variance di ...
References
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External links
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Model selection
Machine learning
Statistical classification
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