The idea behind the complexity-based methods is that if an algorithm chooses from a small set of rules it will probably generalize. Basically, if we have a small set of rules and one of them has small empirical error, the risk of overfitting the data is small since the probability that this event has happened by chance is small. Vice versa if we have a large set of rules and one of them has small empirical error the risk that this event has happened for chance is high.
Complexity-Based Methods
Oneto L.
2020-01-01
Abstract
The idea behind the complexity-based methods is that if an algorithm chooses from a small set of rules it will probably generalize. Basically, if we have a small set of rules and one of them has small empirical error, the risk of overfitting the data is small since the probability that this event has happened by chance is small. Vice versa if we have a large set of rules and one of them has small empirical error the risk that this event has happened for chance is high.
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