Recall & Review
beginner
What is overfitting in machine learning?
Overfitting happens when a model learns the training data too well, including noise and details that don't apply to new data. It performs great on training data but poorly on new, unseen data.
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beginner
What does underfitting mean?
Underfitting occurs when a model is too simple to capture the patterns in the data. It performs poorly on both training and new data because it hasn't learned enough.
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intermediate
How can you tell if a model is overfitting by looking at training and validation errors?
If training error is very low but validation error is high, the model is likely overfitting. It means the model memorized training data but can't generalize well.
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intermediate
Name two common ways to reduce overfitting.
1. Use simpler models with fewer parameters.
2. Use techniques like regularization, dropout, or more training data.
2. Use techniques like regularization, dropout, or more training data.
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beginner
Why is it important to balance between overfitting and underfitting?
Balancing helps the model learn enough from data to make good predictions on new data without memorizing noise or missing important patterns.
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What happens when a model underfits the data?
Which of these is a sign of overfitting?
Which method helps reduce overfitting?
What is the main goal when avoiding underfitting and overfitting?
If a model has high error on training data, what is likely happening?
Explain in your own words what overfitting and underfitting mean and how they affect model performance.
Describe two techniques you can use to prevent overfitting in a machine learning model.