IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

The term "overfitting" in machine learning refers to a model that learns the training data too well, capturing noise and outliers present in that data. This occurs when a model is complex enough to memorize the training dataset rather than learning the underlying patterns that generalize to new, unseen data. As a result, while the model performs exceptionally well on the training set, its performance on validation or test sets typically suffers because it fails to generalize.

Overfitting is a common challenge in machine learning and can be mitigated through various techniques such as regularization, pruning, and using simpler models or cross-validation methods. The recognition of overfitting is crucial for developing robust models that can accurately predict outcomes in real-world scenarios, where data may not exactly match the training examples.

In contrast, a model that generalizes well to unseen data is indicative of a well-balanced model that avoids overfitting. Similarly, models characterized by high bias tend to underfit data, failing to capture the essential structures, rather than overfitting. Lastly, a model that only uses the main features may not necessarily indicate overfitting; it could reflect a well-structured model that focuses on significant input aspects rather than noise or irrelevant data.