Overfitting

Overfitting is a phenomenon in which a machine learning model fits the training data too closely, causing its generalization performance on unseen data (test data or real-world data) to deteriorate significantly. Because the model "learns" even the noise and exceptions in the training data, it appears highly accurate in training but performs poorly in actual predictions. A classic example is an image recognition model that memorizes the backgrounds and shadows in training images, and then fails to correctly classify new images. This is the state of "having memorized without understanding" — the model has failed to capture the underlying patterns. Common causes of overfitting: • The model is too large (too many parameters) • The model is too complex relative to the training data • Too many training epochs • Training data is insufficient or imbalanced • Insufficient regularization or dropout Strategies to prevent overfitting: • Reduce model complexity (avoid excessively deep architectures) • Use early stopping to halt training at the optimal point • Apply L1/L2 regularization • Use techniques such as Dropout to improve generalization • Apply data augmentation and cross-validation • Increase training data or improve its distribution Overfitting, alongside underfitting (insufficient learning), is one of the classic pitfalls to avoid when tuning machine learning models. In practice, monitoring the gap between training accuracy and test accuracy — and building models with appropriate generalization performance — is essential.