Which strategy is commonly used to prevent overfitting in machine learning?

Implementing regularization techniques is a widely recognized method for preventing overfitting in machine learning models. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise. This results in a model that performs well on training data but poorly on unseen data.

Regularization techniques, such as L1 (Lasso) and L2 (Ridge) regularization, add a penalty to the loss function based on the size of the coefficients of the model. By doing so, these techniques discourage the model from becoming overly complex, which helps to maintain a balance between fitting the training data and preserving the model's ability to generalize to new data. Consequently, regularization aids in controlling the complexity of the model, thereby enhancing its performance on unseen datasets.

The other strategies mentioned do not effectively address overfitting. Reducing the dataset size typically exacerbates the issue as it can lead to a lack of sufficient data for the model to learn meaningful patterns. Using more complex models can increase the risk of overfitting by allowing the model to capture noise within the training data. Finally, increasing the number of training epochs can lead to overfitting, as it increases the chance of the model fitting too closely to the