Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
* Option A:"The model is not able to generalize to accommodate new types of data."
* This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting.
* Option B:"The model is too simplistic for the data."
* This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
* Option C:"The model is missing relationships between the inputs and outputs."
* This also aligns more with underfitting, where the model fails to capture important relationships in the data.
* Option D:"The model discards data it considers to be noise or outliers."
* While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them.
* Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data.".
* Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset" Analysis of the Answer Options:ISTQB CT-AI Syllabus References: