In machine learning, overfitting and underfitting are common problems that occur during model training. They affect the accuracy and generalization of the model.
Definition: The model learns the training data too well, including noise and irrelevant details.
Problem: Performs excellently on training data but poorly on new/unseen data.
Causes:
Too complex model (many parameters)
Insufficient training data
Solutions:
Use more training data
Apply regularization (L1, L2)
Prune decision trees
Use simpler models
Example: A student memorizes answers for a specific test but fails a different test on the same topic.
Definition: The model is too simple to capture the underlying patterns in the data.
Problem: Performs poorly on both training and test data.
Causes:
Model is too simple (few parameters)
Important features are missing
Training for too few epochs
Solutions:
Use a more complex model
Add relevant features
Train for more iterations
Example: A student who does not study enough performs poorly in all tests.
| Model Type | Training Accuracy | Test Accuracy | Description |
|---|---|---|---|
| Underfitting | Low | Low | Too simple, fails to capture patterns |
| Good Fit | High | High | Properly captures patterns |
| Overfitting | Very High | Low | Learns noise, fails to generalize |
Take quizzes related to this topic and see where you stand!
Start Quiz Now