terms |
overfit |
As a noun terms
is .
As a verb overfit is
(statistics) to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
overfit |
|
overfit |
overwit |
As verbs the difference between overfit and overwit
is that
overfit is to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data while
overwit is to outwit.
overhit |
overfit |
As verbs the difference between overhit and overfit
is that
overhit is to hit too far or too hard while
overfit is (statistics) to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
overfat |
overfit |
As an adjective overfat
is having too much fat as a proportion of body mass.
As a verb overfit is
(statistics) to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
oversit |
overfit |
As verbs the difference between oversit and overfit
is that
oversit is to preside over, govern, rule; to control while
overfit is to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
As a noun oversit
is governance, authority, possession, control.
overlit |
overfit |
As verbs the difference between overlit and overfit
is that
overlit is (
overlight) while
overfit is (statistics) to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
overfit |
overlearn |
As verbs the difference between overfit and overlearn
is that
overfit is to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data while
overlearn is to learn (something) more than is necessary; to study excessively, to take (something) too much to heart.
overfit |
overparameterize |
see also |
As verbs the difference between overfit and overparameterize
is that
overfit is to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data while
overparameterize is to use an excessive number of parameters.
parameter |
overfit |
As a noun parameter
is a variable kept constant during an experiment, calculation or similar.
As a verb overfit is
to use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
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