Refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data. a. oversampling b. underfitting c. overfitting d. undersampling

c. Overfitting

Step-by-step explanation:

When building a model, the model learns from the data on which it is trained. But, for the model, its accuracy in predicting or explaining the data it was trained is not enough.

Real accuracy of the model can be evaluated when the model faces previously unseen data. The models which are successful on predicting or explaining the training data, but performing significantly less accurate and lacking successfull prediction on new data is said overfitted on training data.

To overcome this phenomenon, some regularization methods can be used Additionally, feature processing like scaling, normalization,removing outliers may help to avoid overfitting.