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Old 08-05-2012, 04:01 AM
rainbow rainbow is offline
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Default Sampling bias and class imbalance for target variable

To avoid sampling bias, the general idea is to have the training distribution to match the testing distribution (as stated in the book). Is this the same as having the sample (train + validation + test) to match the population distribution?

How does this relates to the class imbalance of the target (y) distribution. For instance, training a machine to identify fraud where the number of fraud transactions are much lower than the non-fraud transactions. Is it favourable for the training to upweight the number of fraud transactions in your training data in order to have a balanced data set wrt. y? How does this relates to sampling bias and how do you adjust for this upsampling of fraud cases for the model to generalize well?
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