Microsoft Azure AI Fundamentals (AI-900)Describe fundamental principles of machine learning on AzureMedium
A data scientist is training a machine learning model. During the training process, the model's performance on the training dataset continuously improves, reaching nearly perfect accuracy. However, when the model is evaluated on a separate, unseen validation dataset, its performance is significantly worse. The model struggles to generalize to new data. Which core machine learning concept describes this phenomenon?
- AOverfitting
- BBias
- CUnderfitting
- DVariance
Show answer & explanationAnswer & explanation
Correct answer: A. Overfitting
Overfitting occurs when a model learns the training data too well, including noise and specific patterns, leading to excellent performance on training data but poor generalization to new, unseen data. The model essentially memorizes the training set.
Why the other options are wrong
- B. Bias refers to the error introduced by approximating a real-world problem with a simplified model, often leading to underfitting.
- C. Underfitting occurs when a model is too simple to capture the underlying patterns in the training data, leading to poor performance on both training and validation data.
- D. Variance refers to the amount that the estimate of the target function will change if different training data was used, often associated with overfitting.
Overfitting
A modeling error that occurs when a function is too closely aligned to a limited set of data points. This results in a model that performs very well on the training data but poorly on unseen data.
- High accuracy on training data, low accuracy on test/validation data.
- Model learns noise and specific patterns in the training set.
- Can be mitigated by regularization, more data, or simpler models.
Memory trick: When the brain learns too specifically and can't apply broadly.