# Possible machine learning methodology to implement both continuous features and features with descrete values in the same model

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 ?

• Logistic regression, SVM, random forest, neural networks, Naive Bayes, Cubist, gmb, xgboost and many more are all capable of doing this. – Sycorax Mar 31 '16 at 21:55
• Dear user777 thank you for your answer !! Actually, i didn't knew that gmb (perhaps you meant gbm?) or random forests could handle not only continuous variables of different "nature", but also different "types" of features. In your opinion, also a transformation on the data would be also essential ? especially for the continuous ones ? – Jason Mar 31 '16 at 22:01

logistic_model = glm(Vote ~ Income + Gender, family=binomial, data=mydataset)