Machine Learning Fundamentals

Overfitting

When a model learns training data too well, including noise and outliers, causing poor generalization to new data.

  • Generalization: Explore how Generalization relates to Overfitting
  • Regularization: Explore how Regularization relates to Overfitting
  • Validation: Explore how Validation relates to Overfitting
  • Bias-Variance Tradeoff: Explore how Bias-Variance Tradeoff relates to Overfitting

Why It Matters

Understanding Overfitting is crucial for anyone working with machine learning fundamentals. This concept helps build a foundation for more advanced topics in AI and machine learning.

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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.

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machine-learning-fundamentals generalization regularization validation

Related Terms

Bias-Variance Tradeoff

The balance between a model's bias (systematic error) and variance (sensitivity to training data fluctuations).

Regularization

Techniques to prevent overfitting by adding constraints or penalties to the model (L1, L2, dropout, early stopping).

Added: November 18, 2025

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