nowlet me show you how to read it at each stagewe have decision remember most of our data which is yes or nois going to be or sothe first decision point becomesis employedless than meaning that if we have an employment value of that is nowe're going to go left if employment is that is yeswe're going to go right sowere they previously employedif not go leftif yes go right it turns out that in my sample dataeveryone who is currently employed actually got job offerso can very quickly say if you are currently employedyesyou're worth bringing inwe're going to follow down to the second level here sohow do you interpret thisthe gini score is basically measure of entropy that it' using at each step remember as we're going down the algorithm is trying to minimize the amount of entropy and the samples are the remaining number of samples that haven' beensectioned off by previous decision so say this person was employed the way to read the right leaf node is the value column that tells you at this point we have candidates that were no hires and that were hires so againthe way to interpret the first decision point is if employedwas ' going to go to the rightmeaning that they are currently employedand this brings me to world where everybody got job offer sothat means should hire this person now let' say that this person doesn' currently have job the next thing ' going to look at isdo they have an internship if yesthen we're at point where in our training data everybody got job offer soat that pointwe can say our entropy is now (hjoj )because everyone' the sameand they all got an offer at that point howeveryou know if we keep going down(where the person has not done an internship),we'll be at point where the entropy is it' getting lower and lowerthat' good thing next we're going to look at how much experience they havedo they have less than one year of experienceandif the case is that they do have some experience and they've gotten this far they're pretty good no hire decision we end up at the point where we have zero entropy butall three remaining samples in our training set were no hires we have no hires and hires butif they do have less experiencethen they're probably fresh out of collegethey still might be worth looking at the final thing we're going to look at is whether or not they went to top-tier schooland if sothey end up being good prediction for being hire if notthey end up being no hire we end up with one candidate that fell into that category that was no hire and that were hire whereasin the case candidates did go to top tier schoolwe have no hires and hire