Error analysis provides deeper insights than accuracy alone. Accuracy shows overall performance, but error analysis reveals where the model fails. It helps identify patterns in mistakes, such as bias or overfitting.
Overfitting occurs when a model performs well on training data but poorly on new data. It means the model has learned noise instead of general patterns. Error analysis helps detect overfitting by showing inconsistent performance across datasets.