Is there a way in fitting a decisionTreeClassifier in SKLearn to sparse tuples? The data that I have is based on about 100 features, but only a few of them are ever used to make the decision. Effectively, each row of data is a sparse tuple.
My input data is 30k entries like:
(yes if f1=v1 and f3=v2) (no if f1=v3 and f5=v4) ...
and so on.
There are tens of thousands of entries and the conclusions are not necessarily consistent. My goal is to find an automated method to reduce this down to tens of nodes that approximate the same behaviour. I need access to the resulting structure - which is why I have considered using SKLearn rather than, say DataRobot.
My understanding of the use of SKLearn decisionTreeClassifier is that I have to convert this to a array of tuples of 100 values, and have the features just be given an index number. But, that means I have to provide a value for every one of the 100 features on every row. And that does not well represent the data that I have.
This is like having data to be classified looking like
(yes,v1,__,v2,__,__) (no ,v3,__,__,__,v4)
Hence, the interest is in, for example, being able to list the value of the data as None to indicate that there is no constraint on that field.
The data as given has only equalities. But, the expected output will have inequalities, since that appears to be what SKLearn provides. I would really prefer a decision tree with equalities and inequalities.