What is overfitting? Causes, detection & prevention

Overfitting in machine learning occurs when a model learns the training data too well, including its noise, leading to poor generalization to new data. This results in models that perform exceptionally during training but fail in real-world applications, causing issues such as unreliable predictions in medical systems, excessive false alerts in maintenance, and eroding trust from stakeholders. The problem is rooted in high variance, where models become overly sensitive to data fluctuations. Detecting overfitting involves monitoring discrepancies between training and validation performance, using methods like learning curves, cross-validation, and early stopping. Prevention strategies include regularization techniques, data augmentation, and architectural choices that promote generalization. Tools like Hex facilitate these processes by offering a unified analytics workspace that incorporates validation methodologies, enabling seamless model development and performance tracking.