Dataset
- A structured set of examples used to train or test an algorithm.
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Decision Tree
- Splits data into branches to reach predictions.
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Loss Function
- Calculates how far predictions are from reality.
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Label
- Tags that tell the algorithm what each training example represents.
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Random Forest
- Combines multiple decision trees for better accuracy.
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Gradient Descent
- Adjusts model weights to minimize loss.
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Overfitting
- Model memorizes data instead of generalizing.
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Underfitting
- Model is too simple to capture patterns.
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Cross-Validation
- Splits data into parts to check consistency of results.
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