Overfitting in Machine Learning and Computer Vision

Overfitting in machine learning occurs when a model fits its training data too closely, leading to poor generalization to new, unseen data. This problem arises from various factors, such as high variance and low bias, noisy data, overly complex models, and inadequate training datasets. Detecting overfitting involves evaluating model performance on validation and test data to ensure it generalizes well beyond the training set. Strategies to prevent overfitting include adding more training data, employing data augmentation, standardizing features, selecting essential features, using cross-validation, implementing early stopping, ensembling models, and applying regularization techniques like L1 and L2. While overfitting is generally undesirable, it can sometimes be useful for assessing whether a task is learnable, particularly in computer vision projects aimed at de-risking before full deployment in business applications.

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