Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off

Overfitting occurs when a machine learning model learns training data too well, including noise, which results in poor generalization to new, unseen data. Conversely, underfitting happens when a model is too simplistic to capture the underlying patterns, leading to poor performance on both training and test data. The key to a successful model is finding the balance between these two extremes, often referred to as the bias-variance trade-off. Methods to identify these issues include cross-validation and monitoring errors, while mitigation strategies involve feature engineering, adding more data, using regularization, and adjusting hyperparameters.