Balancing Overfitting and Underfitting in ML (4 Minutes)

Overfitting and underfitting are critical challenges in machine learning that affect model generalization. Overfitting occurs when a model learns noise from training data, leading to poor performance on unseen data, while underfitting arises from a model being too simplistic. Strategies to combat these issues include model simplification, regularization, cross-validation, and data augmentation for overfitting, and increasing model complexity and feature engineering for underfitting. A balanced approach is essential for robust predictive systems.

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