I am running a model for which I am getting a very bad percentage detection of events in the confusion matrix (basically my true positives). Obviously that implies my false negatives are too hight. When I gave these dataset to a neural network node or a decision tree node, I see that the percentage detection by these two techniques is quite good (65% - 67% vis a vis mine 15%). I went to the decision tree diagram and saw the various cuts under which it divides the population. I obviously understand that the variable which falls on the root of the tree has highest importance and the leafs have lowest.
- How can the decision tree "tree" help me create categorical variables or treat continuous variables so that the accuracy of my model improves?
To clarify, if a decision tree can generate a matrix with 65% detection, it would have some rule inside it to get such accuracy. These rules would display in the tree diagram we get as output.
- Can we use this tree to create out variables in a different way and get close to the accuracy given by decision tree?