You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Eliminate features that are highly correlated to the output labels. B. Combine highly co-dependent features into one representative feature. C. Instead of feeding in each feature individually, average their values in batches of 3. D. Remove the features that have null values for more than 50% of the training records.