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Bias-Variance Tradeoff
- Bias refers to the error introduced when a model makes incorrect assumptions about the data.
- Variance measures how much a model’s predictions change when it is trained on different subsets of the data.
- The bias-variance tradeoff illustrates the relationship between model complexity and prediction error.
- Understanding the bias-variance tradeoff is crucial for optimizing machine learning models.
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