Which of these techniques are useful for reducing variance (reducing overfitting)? (Check all that apply.)

The question seems to be missing the list of techniques to choose from. However, I can provide some general techniques that are useful for reducing variance (reducing overfitting):

1. Regularization: This technique adds a penalty term to the loss function to discourage complex models. The most common types of regularization are L1 and L2 regularization.

2. Cross-validation: This technique involves dividing the dataset into multiple subsets and training the model on different combinations of these subsets. This helps to ensure that the model is not overly reliant on any one subset of the data.

3. Ensemble methods: These techniques involve training multiple models and combining their predictions. This can help to reduce overfitting by averaging out the noise in the individual models' predictions.

4. Pruning: This technique is used in decision tree models to remove branches that provide little information gain. This can help to reduce overfitting by simplifying the model.

5. Early stopping: This technique involves stopping the training process before the model has fully converged. This can help to prevent overfitting by preventing the model from becoming too complex.

6. Dropout: This is a technique used in neural networks where a random subset of neurons are "dropped out" or deactivated during each training iteration. This can help to prevent overfitting by ensuring that the model is not overly reliant on any one neuron.

7. Increasing the amount of training data: More data can help the model to generalize better, reducing the chance of overfitting.

8. Feature selection: Removing irrelevant or redundant features can help to simplify the model and reduce the chance of overfitting.

9. Using simpler models: Complex models are more prone to overfitting. Sometimes, using a simpler model can help to reduce overfitting.

Please provide the list of techniques for a more specific answer.