If the algorithm cannot be trained to make perfect prediction it indicates that essential data input features are still lacking. When the predicted values fail to match with observed measurements, despite tuning the algorithm, it means features selection is not yet completed. This is the case when the error between predicted and observed values approaches an asymptote, which is not equal zero. The prediction error is most likely caused by a still hidden object. This hidden object is the cause of the error. But we cannot see the hidden cause yet. However, we can see its consequence, i.e. the error. But since every consequence must have a cause, we must start looking for it.
The missing attributes of hidden feature can be discovered by the mean/median value for that attributes.
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