Define overfitting in ML.

Overfitting in machine learning occurs when a model learns the training data too well, capturing noise and minor fluctuations instead of generalizing from the underlying pattern. This results in excellent performance on training data but poor generalization to unseen data, leading to high variance and low bias.

Overfitting typically happens when a model is overly complex, meaning it has too many parameters relative to the amount of training data. Such models can memorize specific details rather than learn general patterns, causing them to perform poorly on new data. For instance, in deep learning, a neural network with too many layers or neurons may overfit if it lacks sufficient regularization or diverse training examples.

Common signs of overfitting include a significant gap between training and validation accuracy or loss. If training accuracy is very high while validation accuracy is much lower, the model may be memorizing rather than generalizing.

Several techniques help prevent overfitting. Regularization methods like L1 (Lasso) and L2 (Ridge) penalize excessive complexity in models. Dropout randomly deactivates neurons during training to prevent dependency on specific features. Data augmentation, such as rotating or flipping images in computer vision, artificially increases the dataset’s diversity. Cross-validation ensures that a model is tested on multiple subsets of data, reducing the likelihood of overfitting. Additionally, early stopping monitors validation performance and halts training when overfitting starts.

In real-world applications, avoiding overfitting is critical, especially in fields like healthcare, finance, and autonomous systems, where models must generalize well to new data.

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• This topic was modified 1 week, 2 days ago by sakshi009.