AWS AI Practitioner Question 22

If a model hits 99% accuracy on training data but crashes to 58% on new data, the diagnosis is Overfitting. This happens when a model ""memorizes"" the noise and specifics of your training set rather than learning the underlying patterns, much like a student who memorizes a practice test but fails the actual exam. It differs from Underfitting (poor performance on both sets), Data Drift (performance decay over time after deployment), or Hallucination (an LLM-specific error). To fix overfitting, developers use techniques like regularization, early stopping, or providing more diverse training data to force the model to generalize better to real-world scenarios. #AWS #MachineLearning #Overfitting #AIPractitioner #DataScience #TechTips #AWSCertification #KodeKloud