What does "Learning Curves" mean?
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Learning curves are graphs that show how well a model performs as it gets more data or training. They help us see if a model is improving or if it is stuck. Usually, as you give the model more examples, its performance gets better.
How Do They Work?
When you start training a model, it might not understand the task very well. But as it learns from more examples, it begins to get better. The learning curve usually goes up, showing better performance over time.
Why Are They Important?
Learning curves help us make decisions. If the curve is flat, it may mean the model isn't learning much, and we might need to change something. If the curve is going up, it shows that the model is improving, and we can keep training.
Types of Learning Curves
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Training Curve: This shows how well the model does on the training data. If this curve is high, it means the model is learning well from the examples it is given.
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Validation Curve: This shows how well the model performs on new, unseen data. A good model will have both curves going up, but they should not be too far apart.
What Can They Tell Us?
Learning curves can tell us several things:
- If the model needs more data.
- If it is overfitting, meaning it performs well on training data but poorly on new data.
- If the chosen model is the right one for the task.
By looking at learning curves, we can find the best way to train our models and improve their performance.