Now, let's say we want to use a random forest, you know, we're worried that we might be over fitting our training data. It's actually very easy to create a random forest classifier of multiple decision trees.

So, to do that, we can use the same data that we created before. You just need your X and y vectors, that is the set of features and the column that you're trying to predict on:

from sklearn.ensemble import RandomForestClassifier 
 
clf = RandomForestClassifier(n_estimators=10) 
clf = clf.fit(X, y) 
 
#Predict employment of an employed 10-year veteran 
print clf.predict([[10, 1, 4, 0, 0, 0]]) 
#...and an unemployed 10-year veteran 
print clf.predict([[10, 0, 4, 0, 0, 0]]) 

We make a random forest classifier, also available from scikit-learn, and pass it the number of trees we want in our forest. So, we made ten trees in our random forest in the code above. We then fit that to the model.

You don't have to walk through the trees by hand, and when you're dealing with a random forest you can't really do that anyway. So, instead we use the predict() function on the model, that is on the classifier that we made. We pass in a list of all the different features for a given candidate that we want to predict employment for.

If you remember this maps to these columns: Years Experience, Employed?, Previous employers, Level of Education, Top-tier school, and Interned; interpreted as numerical values. We predict the employment of an employed 10-year veteran. We also predict the employment of an unemployed 10-year veteran. And, sure enough, we get a result:

So, in this particular case, we ended up with a hire decision on both. But, what's interesting is there is a random component to that. You don't actually get the same result every time! More often than not, the unemployed person does not get a job offer, and if you keep running this you'll see that's usually the case. But, the random nature of bagging, of bootstrap aggregating each one of those trees, means you're not going to get the same result every time. So, maybe 10 isn't quite enough trees. So, anyway, that's a good lesson to learn here!