Intro to Machine Learning Q&A
Q: What does overfitting exactly mean?
Asked in: Meeting 02
Answer (ChatGPT):
Overfitting is a common problem in machine learning where a model learns not only the underlying patterns in the training data, but also noise or random fluctuations that do not generalize to new data.
Definition
Overfitting occurs when a model performs very well on training data but poorly on unseen (test or validation) data.
Why does it happen?
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The model is too complex (e.g., too many parameters, deep neural nets, high-degree polynomials).
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The training dataset is too small or noisy.
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The model is trained for too many epochs, memorizing examples rather than learning general patterns.
Visual Intuition
Imagine fitting a curve to a scatter plot of points:
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Underfitting: A straight line that doesn’t capture the data trend.
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Just right: A smooth curve that follows the trend.
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Overfitting: A wiggly curve that touches every point but clearly doesn’t represent the true trend.
Example
Let’s say you’re predicting student test scores based on study hours.
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A good model might learn that more study hours → better scores.
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An overfit model might memorize that a student who studied 3.75 hours got 82.4%, and try to reflect every fluctuation like that.
Symptoms of Overfitting
- ✅ High training accuracy
- ❌ Low validation/test accuracy
- ❗ Large gap between training and validation loss
How to Prevent Overfitting
- Use simpler models (fewer parameters)
- Use regularization (like L1/L2, dropout)
- Get more training data
- Apply data augmentation (especially in images)
- Use early stopping during training
- Perform cross-validation
Video Explanations
Regularization in a Neural Network explained Overfitting in a Neural Network explained
Q: What is the purpose of a validation set?
Answer: A validation set is used to compare models. It makes it possible to select the best model and tune the hyperparameters.
Q: What is the train-dev set, when do you need it, and how do you use it?
Answer: The train-dev set is used when there is a risk of mismatch between the training data and the data used in the validation and test datasets (which should always be as close as possible to the data used once the model is in production). The train-dev set is a part of the training set that’s held out (the model is not trained on it). The model is trained on the rest of the training set, and evaluated on both the train-dev set and the validation set. If the model performs well on the training set but not on the train-dev set, then the model is likely overfitting the training set. If it performs well on both the training set and the train-dev set, but not on the validation set, then there is probably a significant data mismatch between the training data and the validation + test data, and you should try to improve the training data to make it look more like the validation + test data.
Video Explanation: