# How to deal with a mix of binary and continuous inputs in neural networks? [duplicate]

I'm using the nnet package in R to attempt to build an ANN to predict real estate prices for condos (personal project). I am new to this and don't have a math background so please bare with me.

I have input variables that are both binary and continuous. For example some binary variables which were originally yes/no were converted to 1/0 for the neural net. Other variables are continuous like Sqft.

Sample of input data

I have normalized all values to be on a 0-1 scale. Maybe Bedrooms and Bathrooms shouldn't be normalized since their range is only 0-4?

Do these mixed inputs present a problem for the ANN? I've gotten okay results, but upon closer examination the weights the ANN has chosen for certain variables don't seem to make sense. My code is below, any suggestions?

ANN <- nnet(Price ~ Sqft + Bedrooms + Bathrooms + Parking2 + Elevator +
Central.AC + Terrace + Washer.Dryer + Doorman + Exercise.Room +
New.York.View,data[1:700,], size=3, maxit=5000, linout=TRUE, decay=.0001)


UPDATE: Based on the comments below regarding breaking out the binary inputs into separate fields for each value class, my code now looks like:

ANN <- nnet(Price ~ Sqft + Studio + X1BR + X2BR + X3BR + X4BR + X1Bath
+ X2Bath + X3Bath + X4bath + Parking.Yes + Parking.No + Elevator.Yes + Elevator.No
+ Central.AC.Yes + Central.AC.No + Terrace.Yes + Terrace.No + Washer.Dryer.Yes
+ Washer.Dryer.No + Doorman.Yes + Doorman.No + Exercise.Room.Yes + Exercise.Room.No
+ New.York.View.Yes + New.York.View.No + Healtch.Club.Yes + Health.Club.No,
data[1:700,], size=12, maxit=50000, decay=.0001)


The hidden nodes in the above code are 12, but I've tried a range of hidden nodes from 3 to 25 and all give worse results than the original parameters I had above in the original code posted. I've also tried it with linear output = true/false.

My guess is that I need to feed the data to nnet in a different way because it's not interpreting the binary input properly. Either that, or I need to give it different parameters.

Any ideas?

• The standard way of using binary or categorical data as neural network inputs is to expand the field to indicator vectors. For instance, if you had a field that could take values 1,2, or 3, then a 1 would be expanded to [1,0,0], 2->[0,1,0], and 3->[0,0,1]. Real valued input is generally kept as-is. – user1149913 Jul 26 '12 at 3:36
• Now that you mention this, I do seem to recall reading this somewhere during my search for an answer. So since the information source is on a csv file, I actually need to add columns to accomodate the new fields for each binary input? For instance if the bedroom input ranges from 0-4, using your example above I'd create 4 additional columns (total of 5 since '0' bedrooms means studio) and a 3BR condo would be expressed as 0,0,0,1,0? – ChrisArmstrong Jul 26 '12 at 13:37