In AdaBoost, how does increasing weights for misclassified data help

Question

Machine Learning

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In AdaBoost, how does increasing weights for misclassified data help

the next learner in classifying them correctly?

0

28.09.2022

3 ответов

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''''''' ''''''' Автор вопроса

Pedro Aguiar
Perhaps because it increases the discrepancy of ou...

but how? let's say it's a binary dataset, and one of them was 1 but was classified as 0, how does increasing weight on this particular data make the classifier predict it as 1?

0

29.09.2022

Pedro Aguiar

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but how? let's say it's a binary dataset, and one ...

Even though it's a binary dataset, the weights aren't binary. Increasing the weights should just increase the distance between the points, not change the trend itself. Hence the outliners will deviate farther from where they are expected to be if they were correctly classified.

0

29.09.2022

Pedro Aguiar · Accepted Answer

Pedro Aguiar

Perhaps because it increases the discrepancy of outliners in comparison with the trend?

0

28.09.2022

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