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Oversampling is done to adjust the ratio of categories represented in your data and can be accomplished with the Oversample Field tool. The classic example is male/female ratio. If you have collected a population sample to train a model and your sample contains 65% males, you may want to oversample the females in your population sample so that your sample closer represents the actual wider 50/50 population.
When this is done, the Score tool needs to know it is dealing with an oversampled value so that it can help correct for the selection bias.
The Help article on the Score tool states: "If this option is checked the user will be asked to provide the level of the target field that was oversampled and the original percentage of the sample that level represented. This information will be used to adjust the fitted probabilities to match the true sample percentages."
So IF you adjusted your data so that the 1's and 0's were at a 50/50 ratio (i.e., you "undersampled" or reduced your 0's to get to that ratio), then you would want to put a "1" in the value and 20 in the percentage. (But if you didn't make any adjustments to your original data, you would not check the option at all.)