# Decision Tree on a set with reliabilty information

I've got an introductory AI course in my university, and I was taught about decision trees. I'm now facing a classification problem that seems solvable with a DT, but I'm stuck with an unseen situation.

Let's say that I'm trying to mimic a function using data from many experimental results, for each outcome my instrument records not only the result but also the reliability, expressed as a probability value.

An extract of my data set looks like this:

inputs |  out  | reliability
------------------------
89,'g' | true  | 0.8
89,'g' | true  | 0.75
89,'g' | false | 0.2
89,'g' | false | 0.1
89,'g' | false | 0.13


What I want to achieve is the creation of a model able to replicate the behavior captured by the instrument, generating the outputs coherently with the given reliability.

Is using a decision tree a bad idea? Otherwise, how should I treat the information about the reliability?

• Is out obtained from reliability, by something like: if reliability <= 0.5 then out=true, else out = false? Because in that case you could model reliability directly and ignore out. Feb 5 '19 at 12:48

For example if your dataset is relatively small you could oversample by simply replicating each point floor(10 * r) times where r is the reliability - so 0 ≤ r < 0.1 would get no additional replicates, 0.1 ≤ r < 0.2 would get one, and so on.