soyou can see we just keep going until we reach an entropy of if at all possiblefor every case ensemble learning using random forest nowlet' say we want to use random forestyou knowwe're worried that we might be over fitting our training data it' actually very easy to create random forest classifier of multiple decision trees soto do thatwe can use the same data that we created before you just need your and vectorsthat is the set of features and the column that you're trying to predict ongspntlmfbsofotfncmfjnqpsu boepn'psftu$mbttjgjfs dmg boepn'psftu$mbttjgjfs @ftujnbupst dmg dmggju   sfejdufnqmpznfoupgbofnqmpzfezfbswfufsbo qsjoudmgqsfejdu boebovofnqmpzfezfbswfufsbo qsjoudmgqsfejdu we make random forest classifieralso available from scikit-learnand pass it the number of trees we want in our forest sowe made ten trees in our random forest in the code above we then fit that to the model you don' have to walk through the trees by handand when you're dealing with random forest you can' really do that anyway soinstead we use the qsfejdu function on the modelthat is on the classifier that we made we pass in list of all the different features for given candidate that we want to predict employment for if you remember this maps to these columnsyears experienceemployed?previous employerslevel of educationtop-tier schooland internedinterpreted as numerical values we predict the employment of an employed -year veteran we also predict the employment of an unemployed -year veteran andsure enoughwe get result