Question

A data science team trains a model to predict loan defaults. During evaluation, the model achieves 99% accuracy on the training data but only 62% accuracy on the held-out test data. What is the most likely explanation for this result?

Accepted Answer

The model is overfitting, having memorized the training data rather than learning patterns that generalize to new data

Answer

The model is underfitting because it is too simple to capture the underlying patterns

Answer

The test dataset is too large relative to the training dataset

Answer

The model needs to be switched from supervised learning to unsupervised learning