Overfitting in machine learning occurs when a model learns the training data too well, capturing noise and irrelevant patterns instead of general trends. This leads to excellent performance on the training dataset but poor generalization to new, unseen data. Overfitting happens when a model is too complex relative to the amount of training data, often due to excessive parameters or insufficient regularization.

One common sign of overfitting is a large gap between training and validation accuracy. The model performs exceptionally well on training data but struggles with test data, indicating that it has memorized specific data points rather than understanding underlying patterns.

Several techniques can help prevent overfitting. Regularization methods like L1 (Lasso) and L2 (Ridge) add penalties to large coefficients, preventing the model from relying too much on specific features. Dropout, a technique used in neural networks, randomly disables some neurons during training to encourage generalization. Cross-validation helps evaluate model performance by training on different subsets of data, reducing the risk of overfitting.

Another effective approach is gathering more diverse training data. A larger dataset helps the model learn generalized patterns rather than memorizing noise. Feature selection, which involves removing irrelevant or redundant variables, can also improve model generalization.

Overfitting is a major challenge in machine learning, but by applying proper techniques such as regularization, cross-validation, and data augmentation, developers can build models that perform well on real-world data. Understanding overfitting and its solutions is crucial for anyone working with machine learning models. To gain deeper insights into handling overfitting and other challenges, consider enrolling in a data science and machine learning course to enhance your expertise.