Question 3If your Neural Network model seems to have high variance, what of the following would be promising things to try?

If your Neural Network model appears to have high variance, here are some promising strategies to try:

1. Get more training data: High variance typically means that your model is overfitting the training data. It's learning the noise in the training data instead of the actual patterns. More training data can help the model generalize better.

2. Try a smaller set of features: Reducing the number of features can simplify the model and help reduce overfitting.

3. Increase Regularization: Regularization techniques such as L1 and L2 regularization can help prevent overfitting by adding a penalty for complexity in the loss function.

4. Change the architecture of your neural network: You could try to reduce the number of layers or the number of neurons per layer. A simpler model might generalize better.

5. Early stopping: During the training process, keep track of the model's performance on a validation set. Stop training when the performance on the validation set starts to degrade.

6. Use Dropout: Dropout is a regularization technique for reducing overfitting in neural networks. It works by randomly "dropping out" or turning off certain neurons during training, which helps to prevent the model from becoming too dependent on any one feature.

Remember, it's important to use a validation set or cross-validation to tune these parameters and check if these changes are improving the model's performance.