Bad data invariably leads to bad results. Even if you’ve taken care to eliminate errors from your training data, you can still make mistakes with your algorithms. The ultimate goal of any predictive model is to make accurate predictions about unseen data. A good model should first extrapolate patterns from your training data to correctly predict outcomes with reasonable accuracy, then, it should be able to generalize, applying what it has learned to make reasonably accurate predictions on new data.
