Which of the following options is an example of the concept of overfitting?

Question

Options

• AA model for predicting academic performance was trained with data from students at one
• BA model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On
• CA previously trained model for recognizing cars is adapted and extended so that it can also
• DA model for predicting IT system failures delivers too many false-negative predictions because

• A
  76% (29)
• B
  5% (2)
• C
  13% (5)
• D
  5% (2)

Explanation

The ISTQB CT-AI syllabus defines overfitting in Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely--including noise and irrelevant detail-- resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. Option A perfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across

No community discussion yet for this question.