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Experimentally data modeled using ANNs was validated against unseen data.
There were good overlap between network estimations and unseen data.
Today the government has published previously unseen data on the size of our property estate.
Further analysis indicates that our proposed approach is effective in processing unseen data and features.
Both approaches result in very good estimations of the mean remaining useful life of unseen data.
Unseen data points were used to check generalization capability of the best network.
For external validation, we applied our Stanford classifier to unseen data from Geisinger.
How much data is needed to train a model that generalizes well to unseen data?
Opening up the previously unseen, data science is a revolution that some have compared to the invention of the microscope.
Which samples should I collect for training and how many are sufficient to generalize conclusions to unseen data?
One fundamental challenge when working with messy real-world data is handling diverse and possibly unseen data schemas.
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