we can walk through this and see what it means soin our output decision tree we actually end up with depth of fourwith nine different nodesandagainif we remind ourselves what these different fields correlate tothe way to read this isif (feature in )so that means if the employed is nothen we drop down to feature this list is zero-basedso feature in our excel document is internships we can run through the tree like thatthis person is not currently employeddid not do an internshiphas no prior years of experience and has bachelor' degreewe would not hire this person then we get to the else clauses if that person had an advanced degreewe would hire themjust based on the data that we had that we trained it on soyou can work out what these different feature ids mean back to your original source datarememberyou always start counting at and interpret that accordingly note that all the categorical features are expressed in boolean in this list of possible categories that it sawwhereas continuous data is expressed numerically as less than or greater than relationships and there you have itan actual decision tree built using spark and mllib that actually works and makes sense pretty awesome stuff -means clustering in spark alrightlet' look at another example of using spark in mlliband this time we're going to look at -means clusteringand just like we did with decision treeswe're going to take the same example that we did using scikit-learn and we're going to do it in spark insteadso it can actually scale up to massive dataset soagaini've made sure to close out of everything elseand ' going to go into my book materials and open up the qbslfbot python scriptand let' study what' going on in