Explanation:

High variance (overfitting) occurs when a model is too complex and learns the noise in the training data rather than the underlying pattern. To reduce high variance:

1. Add dropout - This is a regularization technique that randomly drops units during training, preventing the model from becoming too reliant on specific neurons and reducing overfitting.

2. Reduce number of parameters - Simplifying the model architecture by reducing the number of layers or neurons makes the model less complex and less prone to overfitting.

Why other options are incorrect:

• A. Use a more complex model - This would actually increase variance (overfitting) rather than reduce it.
• C. Increase learning rate - This affects the training speed and convergence but doesn't directly address variance issues. It might even cause instability in training.
• D. Reduce dataset size - This would likely increase variance because with less data, the model is more likely to overfit to the limited training examples.

Additional methods to reduce high variance:

• Regularization techniques (L1, L2)
• Early stopping
• Data augmentation
• Cross-validation
• Ensemble methods (bagging)