soin this particular casewe ended up with hire decision on both butwhat' interesting is there is random component to that you don' actually get the same result every timemore often than notthe unemployed person does not get job offerand if you keep running this you'll see that' usually the case butthe random nature of baggingof bootstrap aggregating each one of those treesmeans you're not going to get the same result every time somaybe isn' quite enough trees soanywaythat' good lesson to learn hereactivity for an activityif you want to go back and play with thismess around with my input data go ahead and edit the code we've been exploringand create an alternate universe where it' topsy turvy worldfor exampleeveryone that gave job offer to now doesn' get one and vice versa see what that does to your decision tree just mess around with it and see what you can do and try to interpret the results sothat' decision trees and random forestsone of the more interesting bits of machine learningin my opinion always think it' pretty cool to just generate flowchart out of thin air like that sohopefully you'll find that useful ensemble learning when we talked about random foreststhat was an example of ensemble learningwhere we're actually combining multiple models together to come up with better result than any single model could come up with solet' learn about that in little bit more depth let' talk about ensemble learning little bit more soremember random forestswe had bunch of decision trees that were using different subsamples of the input dataand different sets of attributes that it would branch onand they all voted on the final result when you were trying to classify something at the end that' an example of ensemble learning another examplewhen we were talking about kmeans clusteringwe had the idea of maybe using different -means models with different initial random centroidsand letting them all vote on the final result as well that is also an example of ensemble learning