IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

Overfitting in machine learning occurs when a model has learned the details and noise of the training data to such an extent that it negatively impacts its performance on new, unseen data. This means that while the model may perform exceptionally well on the training dataset—capturing all the nuances and patterns—it fails to generalize those findings to other datasets. When a model is overfitted, it excels at transforming the specific training data but lacks the ability to perform accurately when exposed to different, unseen data, leading to poor overall performance and reliability.

In contrast, a model that generalizes well would be able to apply the knowledge gained during training to effectively predict outcomes on new data. Recognizing overfitting is crucial for developing robust machine learning solutions that maintain predictive power in real-world applications.