Overfitting is a common problem in machine learning where a model is too complex and fits the training data too closely, capturing the noise or random fluctuations in the data instead of the underlying pattern. This can result in a model that performs well on the training data but poorly on new, unseen data, leading to poor generalization performance.

To prevent overfitting, there are several techniques that data scientists can use :

Regularization : Regularization is a technique that adds a penalty term to the loss function of the model to reduce its complexity and prevent overfitting. Common regularization methods include L1 (Lasso) and L2 (Ridge) regularization.

Cross-validation : Cross-validation is a technique that involves dividing the dataset into multiple parts and using one part for testing and the other parts for training. This allows the data scientist to assess the model's performance on multiple partitions of the data, helping to prevent overfitting.

Early stopping : Early stopping is a technique used in deep learning to prevent overfitting by monitoring the performance on a validation set during training and stopping the training process when the performance on the validation set stops improving.

Ensemble methods : Ensemble methods, such as bagging and boosting, can be used to prevent overfitting by combining the predictions of multiple models.

Simplifying the model : Reducing the complexity of the model, for example, by using a simpler model architecture or fewer features, can help prevent overfitting.

Adding more data : Increasing the size of the training dataset can help prevent overfitting by providing the model with more information to learn from and reducing the impact of any noise in the data.