In the world of analytics,modeling is a general term used to refer to the use of data mining (machine learning) methods to develop predictions. If you want to know what ad a particular user is more likely to click on, or which customers are likely to leave you for a competitor, you develop a predictive model.
There are a lot of models to choose from: Regression, Decision Trees, K Nearest Neighbor, Neural Nets, etc. They all will provide you with a prediction, but some will do better than others depending on the data you are working with. While there are certain tricks and tweaks one can do to improve the accuracy of these models, it never hurts to remember the fact that there is wisdom to be found in the masses.
The Jelly Bean Jar
I am sure everyone has come across some version of this in their life: you are at a fair or school fund raising event and someone has a large see-through jar full of jelly beans (or marbles or nickles). Next to the jar are some slips of paper with the instructions to “Guess the number of jelly beans in the jar you win!”
An interesting thing about this game, and you can try this out for yourself, is that given a reasonable number of participants, more often than not, the average guess of the group will perform better than the best individual guesser. Or in other words, imagine there are 200 jelly beans in the jar and the best guesser (the winner) guesses 215. More often than not, the average of all the guesses might be something like 210 or 190. The group cancels out their over and under guessing, resulting in a better answer than anyone individual.
How Do We Get the Average in Models?
There are countless ways to do it, and researchers are constantly trying new approaches to get that extra 2% improvement over the last model. For ease of understanding though, I am going to focus on 2 very popular methods of ensemble modeling : Random Forests & Boosted Trees.
Imagine you have a data set containing 50,000 records. We will start by randomly selecting 1000 records and creating a decision tree from those records. We will then put the records back into the data set and draw another 1000 records, creating another decision tree. The process is repeated over and over again for a predefined number of iterations (each time the data used is returned to the pool where it could possibly be picked again).
After all the sample decision trees have been created (let’s say we created 500 for the sake of argument), the model then takes the mean or average of all the models if you are looking at a regression or the mode of all the models if you are dealing with a classification.
For those unfamiliar with the terminology, a regression model looks for a numeric value as the answer. It could be the selling price of a house, a person’s weight, the price of a stock, etc. While a classification looks for classifying answers: yes or no, large – medium – small, fast or slow, etc.
Another popular method of ensemble modeling is known as boosted trees. In this method, a simple (poor learner) model tree is created – usually 3-5 splits maybe. Then another small tree (3-5 splits) is built by using incorrect predictions for the first tree. This is repeated multiple times (say 50 in this example), building layers of trees, each one getting a little bit better than the one before it. All the layers are combined to make the final predictive model.
Now I know this may be an oversimplified explanation, and I will create some tutorials on actually building ensemble models, but sometimes I think just getting a feel for the concept is important.
So are ensemble models always the best? Not necessarily.
One thing you will learn when it comes to modeling is that no one method is the best. Each has their own strengths. The more complex the model, the longer it takes to run, so sometimes you will find speed outweighs desire for the added 2% accuracy bump. The secret is to be familiar with the different models, and to try them out in different scenarios. You will find that choosing the right model can be as much of an art as a science.