What is bias-variance tradeoff?
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The bias-variance tradeoff is a fundamental concept in machine learning that describes the balance between two types of prediction errors:
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Bias – Error due to overly simplistic models that make strong assumptions. High bias means the model underfits, failing to capture patterns in the data.
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Variance – Error due to overly complex models that are sensitive to small fluctuations in the training data. High variance means the model overfits, capturing noise instead of general patterns.
Tradeoff:
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Increasing model complexity reduces bias but increases variance.
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Decreasing complexity increases bias but reduces variance.
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The goal is to find an optimal complexity where total error (bias² + variance + irreducible error) is minimal.
Example:
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High Bias: Linear regression on nonlinear data.
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High Variance: Deep decision tree trained on small dataset.
Solution: Use techniques like cross-validation, regularization, and more data to balance bias and variance, improving model generalization.
In short, bias-variance tradeoff is about choosing a model that’s neither too simple nor too complex.
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