Which of the following techniques can help prevent overfitting in regression models?

To prevent overfitting in regression models, there are several techniques that can be employed. Here are some steps you can take:

1. Split the data: Divide your dataset into two parts - a training set and a validation set. The training set will be used to train the model, while the validation set will be used to evaluate its performance.

2. Regularization: Apply regularization techniques such as Ridge regression or Lasso regression. These techniques add a penalty term to the loss function, which helps to control the complexity of the model and prevent overfitting.

3. Cross-validation: Use cross-validation techniques, such as k-fold cross-validation, to assess the model's performance on different subsets of the data. This helps to ensure that the model's performance is not overly influenced by a specific subset of the data.

4. Feature selection: Select only the most relevant features for your regression model. Removing irrelevant or redundant features can help to reduce overfitting.

5. Increase the amount of data: If possible, gather more data to train your model. Having a larger dataset can help to generalize better and reduce overfitting.

6. Early stopping: Monitor the model's performance on the validation set during training. If the performance starts to deteriorate, stop the training process early to prevent overfitting.

By following these steps, you can help prevent overfitting in regression models and improve their generalization performance.