« Estimation de l'erreur de prédiction » : différence entre les versions
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'''expected error estimate''' | '''expected error estimate''' | ||
'''Laplace error estimate''' | |||
In pruning a decision tree, one needs to be able to estimate the expected error at any node (branch or leaf). This can be done using the '''Laplace error estimate''', which is given by the formula<center>''E''(''S'') = (''N'' – ''n'' + ''k'' – 1) / (''N'' + ''k'').</center>where | In pruning a decision tree, one needs to be able to estimate the expected error at any node (branch or leaf). This can be done using the '''Laplace error estimate''', which is given by the formula<center>''E''(''S'') = (''N'' – ''n'' + ''k'' – 1) / (''N'' + ''k'').</center>where | ||
Version du 13 septembre 2019 à 10:42
en construction
Définition
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Français
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Anglais
expected error estimate
Laplace error estimate
In pruning a decision tree, one needs to be able to estimate the expected error at any node (branch or leaf). This can be done using the Laplace error estimate, which is given by the formula
where
| S | is the set of instances in a node |
| k | is the number of classes (e.g. 2 if instances are just being classified into 2 classes: say positive and negative) |
| N | is the is the number of instances in S |
| C | is the majority class in S |
| n | out of N examples in S belong to C |
Contributeurs: Claude Coulombe, Patrick Drouin, wiki
