What is overfitting in ML?

Overfitting in machine learning refers to a scenario where a model learns the details and noise in the training data to the extent that it negatively impacts the performance of the model on new, unseen data. In other words, an overfitted model captures the noise in the training data as if it were real patterns, leading to poor generalization to new data.

How it occurs

Overfitting often occurs when a model is too complex relative to the amount and noisiness of the training data. High model complexity can enable the model to capture the noise in the data as if it were signal, leading to overfitting.

Strategies to prevent overfitting

• Feature selection: Choose relevant features that are most informative for the problem at hand.
• Regularization: Add a penalty term to the model's loss function to discourage overly complex models.
• Cross-validation: Split the data into multiple training and validation sets to assess the model's performance more accurately.
• Hyperparameter tuning: Optimize the model's hyperparameters to find the best balance between bias and variance.

By balancing model complexity and using the right strategies such as feature selection, regularization, cross-validation, and hyperparameter tuning, you can prevent overfitting and improve the model's generalization ability for more accurate predictions.