Microsoft Azure AI Fundamentals (AI-900)Describe fundamental principles of machine learning on AzureMedium
A data scientist observes that their machine learning model performs exceptionally well on the training data, achieving very high accuracy. However, when the model is tested on new, unseen data, its performance drops significantly, and it generalizes poorly. This indicates that the model has learned the training data too specifically, including noise and irrelevant details. What is this phenomenon called?
- ABias
- BUnderfitting
- CVariance
- DOverfitting
Show answer & explanationAnswer & explanation
Correct answer: D. Overfitting
Overfitting occurs when a model learns the training data too well, capturing noise and specific patterns that do not generalize to new, unseen data. This results in high performance on training data but poor performance on test data.
Why the other options are wrong
- A. Bias refers to the error introduced by approximating a real-world problem with a simplified model, often leading to underfitting.
- B. Underfitting occurs when a model is too simple to capture the underlying patterns in the data, performing poorly on both training and test data.
- C. Variance refers to the amount that the model's prediction would change if trained on a different dataset, often associated with overfitting.
Overfitting
Overfitting occurs when a machine learning model learns the training data and its noise too well, to the extent that it negatively impacts the model's performance on new, unseen data. It essentially memorizes the training examples rather than learning generalizable patterns.
- High performance on training data, low on test data.
- Model is too complex for the data.
- Captures noise and irrelevant details.
- Reduced by regularization, more data, feature selection, early stopping.
Memory trick: Generalization is like a 'Goldilocks' balance: not too simple (underfit), not too complex (overfit).