Bias-Variance Tradeoff
The Bias-Variance Tradeoff is a core machine learning concept describing the conflict between a model's ability to minimize bias (errors from simple assumptions) and variance (errors from sensitivity to training data fluctuations). Balancing them is key to avoiding overfitting or underfitting.
Frequently Asked Questions
What is high bias?▼
High bias causes underfitting; the model is too simple to capture the underlying patterns in the dataset.
What is high variance?▼
High variance causes overfitting; the model learns noise in the training set and fails to generalize to test data.
Quick Facts
- CategoryModel Training
- Key ApplicationModel evaluation, validation, and regularized training
Coverage Trend12 Weeks
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