continuing to move through our %fdjtjpo sffusbjo$mbttjgjfs callwe are going to use the hjoj impurity metric as we measure the entropy we have nby%fqui of which is just an upper boundary on how far we're going to gothat can be larger if you wish finallynby#jot is just way to trade off computational expense if you canso it just needs to at least be the maximum number of categories you have in each feature remembernothing really happens until we call an actionso we're going to actually use this model to make prediction for our test candidate we use our %fdjtjpo sff modelwhich contains decision tree that was trained on our test training dataand we tell that to make prediction on our test dataqsfejdujpot npefmqsfejdu uftu%bub qsjou )jsfqsfejdujpo sftvmut qsfejdujpotdpmmfdu gpssftvmujosftvmut qsjou sftvmu we'll get back list of predictions that we can then iterate through soqsfejdu returns plain old python object and is an action that can dpmmfdu let me rephrase that little bitdpmmfdu will return python object on our predictionsand then we can iterate through every item in that list and print the result of the prediction we can also print out the decision tree itself by using up%fcvh usjohqsjou -fbsofedmbttjgjdbujpousffnpefm qsjou npefmup%fcvh usjoh that will actually print out little representation of the decision tree that it created internallythat you can follow through in your own head sothat' kind of cool too running the script alrightfeel free to take some timestare at this script little bit moredigest what' going onbutif you're readylet' move on and actually run this beast soto do soyou can' just run it directly from canopy we're going to go to the tools menu and open up canopy command promptand this just opens up windows command prompt with all the necessary environment variables in place for running python scripts in canopy make sure that the working directory is the directory that you installed all of the course materials into