Start writing here...
Great question! Overfitting and underfitting are two common problems in machine learning that affect how well a model performs, especially on new, unseen data.
🔴 Overfitting
Definition: When a model learns the training data too well, including its noise and random fluctuations, leading to poor performance on new data.
-
Signs:
- High accuracy on training data
- Low accuracy on validation/test data
-
Causes:
- Model is too complex (e.g., too many parameters)
- Too little training data
- Noisy data
-
Solutions:
- Simplify the model
- Use more training data
- Apply regularization (like L1 or L2)
- Use dropout (for neural networks)
- Cross-validation
🔵 Underfitting
Definition: When a model is too simple to capture the underlying structure of the data, resulting in poor performance even on the training data.
-
Signs:
- Low accuracy on both training and test data
-
Causes:
- Model is too simple
- Features are not informative
- Insufficient training
-
Solutions:
- Increase model complexity
- Add better features
- Train for longer
- Reduce regularization
Quick Visual Metaphor:
- Overfitting: Memorizing answers without understanding concepts.
- Underfitting: Not learning enough to even memorize basic facts.
Want a quick graph that shows this visually (like a U-shaped curve of model complexity vs error)?