variable is variable that combines other variables for instance "dataand "sciencemight be good predictors in their own right but probably the two of them co-occurring in the same text might have its own value bucket is somewhat the opposite instead of combining two variablesa variable is split into multiple new ones this makes sense for numerical variables figure shows what decision tree might look like and where you can find interaction and bucketing car insurance decision treeprobability of an insuree crashing the car within year gender female male car color other interaction of male and "< age > red < - age has been split into buckets"> "" - "and "< figure fictitious decision tree model decision tree automatically creates buckets and supposes interactions between input variables whereas naive bayes supposes independence of all the input variablesa decision tree is built upon the assumption of interdependence but how does it build this structurea decision tree has few possible criteria it can use to split into branches and decide which variables are more important (are closer to the root of the treethan others the one we'll use in the nltk decision tree classifier is "information gain to understand information gainwe first need to look at entropy entropy is measure of unpredictability or chaos simple example would be the gender of baby when woman is pregnantthe gender of the fetus can be male or femalebut we don' know which one it is if you were to guessyou have chance to guess correctly (give or takebecause gender distribution isn' uniformhoweverduring the pregnancy you have the opportunity to do an ultrasound to determine the gender of the fetus an ultrasound is never conclusivebut the farther along in fetal developmentthe more accurate it becomes this accuracy gainor information gainis there because uncertainty or entropy drops let' say an ultrasound at weeks pregnancy has accuracy in determining the gender of the baby uncertainty still existsbut the ultrasound did reduce the uncertainty