Other Ques From: Ch.1 from the 'Hundred-Page Machine Learning Book'
What assumptions are made about training data to ensure a model generalizes well?
To ensure a model generalizes well — meaning it performs well on unseen data — machine learning makes some key assumptions about the training data:
1️⃣ Independence of Examples
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Training samples are assumed to be independent of each other (no autocorrelation unless explicitly modeled, e.g., in time series).
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This avoids misleading patterns caused by dependencies between observations.
2️⃣ Identically Distributed (i.i.d.)
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All training and test data are drawn from the same probability distribution.
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This ensures the patterns the model learns are relevant for future predictions.
3️⃣ Representative Sampling
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The training data should be representative of the real-world population.
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Missing subgroups or skewed sampling can cause bias and poor performance on certain cases.
4️⃣ Sufficient Size
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The dataset should be large enough to cover the natural variability in the data.
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Small datasets increase the risk of overfitting to noise.
5️⃣ Correct and Consistent Labels (in supervised learning)
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Labels should be accurate; mislabeled data acts like noise and can degrade model accuracy.
💡 If any of these assumptions is violated — for example, if the training and test sets come from different distributions (dataset shift) — the model’s generalization ability can drop significantly.
If you want, I can also share 5 warning signs during training that indicate your generalization assumptions might be failing. Would you like me to do that?
Here are 5 warning signs that the generalization assumptions might be breaking down during training:
1️⃣ Large Gap Between Training and Validation Performance
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Symptom: High accuracy (or low loss) on the training set but much worse results on validation/test sets.
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Possible Cause: Overfitting due to non-representative training data or too much noise.
2️⃣ Sudden Drop in Performance on New Data
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Symptom: Model works well on historical data but fails when deployed.
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Possible Cause: Data drift — the real-world data distribution has shifted away from the training distribution.
3️⃣ High Variance Across Cross-Validation Folds
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Symptom: Performance varies significantly from fold to fold.
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Possible Cause: Training data might not be independent or is not evenly representative of all cases.
4️⃣ Very High Model Complexity Without Performance Gain
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Symptom: Adding more parameters or layers increases training accuracy but validation accuracy stays the same or drops.
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Possible Cause: Model is memorizing training data rather than learning general patterns.
5️⃣ Poor Performance on Minority Subgroups
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Symptom: Model works well overall but fails for specific segments of the population.
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Possible Cause: Training data underrepresents those subgroups, breaking the "representative sampling" assumption.
If you want, I can give you a table of these warning signs + possible remedies so you can keep it handy for ML project reviews.
Do you want me to prepare that table?
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