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In order to assess predictability of the model it should bee validated on the data outside the Training dataset, which is called Validation dataset.
If the If model discrepancy on Validation dataset is close to the to model discrepancy on Training dataset one can say that a given model has a good predictability within the Source Dataset range.
If model discrepancy on Validation dataset is not close to model discrepancy on Training dataset and this is called overtraining and means that a given model realization has "remembered" the Training dataset but can not accurately predict on the data points outside the Training dataset.
Splitting the Source Dataset into Training dataset and Validation dataset can be done in different ways.
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