You would like to build a classifier for an Autism early detection application. Each data point in the dataset represents a patient. Each patient is described by a set of attributes such as age, sex, ethnicity, communication and development figures, etc. You know from domain knowledge that autism is more prevalent in males than females.

If the dataset you are using to build the classifier is noisy, contains redundant attributes and missing values. If you are considering a decision tree classifier and a k-nearest neighbor classifier, explain how each of these can handle the three mentioned problems:

1. Noise

2. Missing Values

3. Redundant Attributes