now that technique of randomly resampling our data with the same model is term called bootstrap aggregatingor bagging this is form of what we call ensemble learningwhich we'll cover in more detail shortly but the basic idea is that we have multiple treesa forest of trees if you willeach that uses random subsample of the data that we have to train on then each of these trees can vote on the final resultand that will help us combat overfitting for given set of training data the other thing random forests can do is actually restrict the number of attributes that it can choosebetween at each stagewhile it is trying to minimize the entropy as it goes and we can randomly pick which attributes it can choose from at each level so that also gives us more variation from tree to treeand therefore we get more of variety of algorithms that can compete with each other they can all vote on the final result using slightly different approaches to arriving at the same answer so that' how random forests work basicallyit is forest of decision trees where they are drawing from different samples and also different sets of attributes at each stage that it can choose between sowith all thatlet' go make some decision trees we'll use random forests as well when we're donebecause scikit-learn makes it really really easy to doas you'll see soon decision trees predicting hiring decisions using python turns out that it' easy to make decision treesin fact it' crazy just how easy it iswith just few lines of python code so let' give it try 've included btu)jsftdtw file with your book materialsand that just includes some fabricated datathat made upabout people that either got job offer or not based on the attributes of those candidates jnqpsuovnqzbtoq jnqpsuqboebtbtqe gspntlmfbsojnqpsuusff joqvu@gjmf  tqbsl%bub djfodf btu)jsftdtweg qesfbe@dtw joqvu@gjmfifbefs