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In imbalanced datasets, wherever the sampling ratio does not follow the inhabitants statistics, you can resample the dataset in a conservative fashion named minimax sampling. The minimax sampling has its origin in Anderson minimax ratio whose worth is proved to get 0.5: in a binary classification, the class-sample sizes read what he said really should be picked out equally. This ratio could be proved being minimax ratio only less than the assumption of LDA classifier with Gaussian distributions.
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It Is that this 2nd step which makes the strategy one among non-chance sampling. In quota sampling the selection with the sample is non-random. For example, interviewers might be tempted to interview those that look most helpful.
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May be the problem currently being questioned with the study one which can sufficiently be answered utilizing a comfort sample?
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