Correct Answer: C When a model performs well on training data but poorly on unseen data

Sat, 18 Apr 2026 09:33:52 +0000

Correct Answer: When a Model Performs Well on Training Data but Poorly on Unseen Data – Understanding Overfitting in Machine Learning In the world of machine learning, achieving high performance on training data is a common goal—but it’s not always a sign of a robust model. One of the most critical challenges developers face is when a model excels during training yet fails when exposed to new, unseen data. The correct answer to this problem lies in the concept of overfitting—a common issue where a model learns the training data too well, including its noise and outliers, rather than generalizing patterns that apply across broader datasets. ### What is Overfitting? Overfitting occurs when a predictive model captures not only the underlying relationships in the training data but also random fluctuations, noise, or irrelevant details. As a result, the model becomes overly complex and performs exceptionally on known data but fails to predict accurately on new inputs—such as real-world scenarios or test sets. This discrepancy between training accuracy and generalization is a key indicator of overfitting.