I would like to implement a machine learning procedure, in order to predict a categorical binary outcome. However, my main concern, is the different "nature" of my features: while a proportion of my features have discrete binary values, such as "yes" or "no", the others are essentially continuous numeric variables. Thus, as im mainly working in R, is there a package or methodology that can handle both "types" of features into training a classifier ? or i have to separare my data set into two datasets, each comprised of the different type of features ?
You can have both continuous and binary values when making a classifier. You're model will have what is known as dummy variables in it. As a first pass, I would give a go at logistic regression, but there are many, many different types of ways to do this.
Let's say you had a problem wanting to predict if someone will vote (so your binary outcomes) and you have two features: gender and income. In r you would try something like:
logistic_model = glm(Vote ~ Income + Gender, family=binomial, data=mydataset)