What is Random Forest?

I just can't decide. Should I play a round of golf today? Let's make a decision tree to decide. First off do I have the time? Is it sunny? Do I have my clubs with me? This decision tree is an example of a classification problem, where the class labels are "golf" and "no golf". While they are helpful, decision trees can be prone to problems, such as bias and overfitting. That's where random forest can help.?Random forest is a type of machine learning model that uses an ensemble of decision trees to make its predictions. And why do we call it random forest?

Can't see the forest for the (decision) trees

Well, the reason is because it's actually built by taking a random sample of my data and then building an ongoing series of decision trees on those subsets. So, we're essentially creating a bunch of smaller decision trees that work together as one larger model or group. Chances are other people have built different, and maybe better, decision trees to answer the same question. Maybe those trees consider things like time of day, or the difficulty of the course. The more decision trees I use with different criteria, the better my random forest will perform because it's essentially increasing my prediction accuracy. And if one or two of these smaller decision trees are not relevant on a certain day then that information is tossed out and not used to make the overall prediction.

One of the primary benefits of random forest is that it can reduce overfitting, which occurs when your model starts to memorize the data rather than trying to generalize for making predictions on future data. Essentially, it helps me get around the limitations of my data, which might not be fully representative of all golfers, or the best features to use in my model. It can also help reduce bias which can occur when there is a certain degree of error introduced into your model. Bias occurs when you are not evenly splitting your instance space during training, so instead of seeing all of my data points, you might only see half because of how I set up my model.

How many trees is too many?

To set up a random forest algorithm, you'll set parameters for node size, number of trees, and number of features to randomly sample from your training data set. It can be challenging at first because you'll want a lot of trees to get the best predictive accuracy, but you don't want too many trees because it will take a long time to train the model and use a lot of memory space.

Once you set up your parameters, you'll use a random forest model to make predictions on your test data, and you can even segment or slice your results by different criteria. Maybe you want to know how your random forest does on certain types of golf courses, or how it performs during different times of day.?

It's not just about a tee time

Random forest is pretty popular among data science professionals, and with good reason. It can be extremely helpful in all sorts of classification problems. In finance it can be used to predict the likelihood of default. In medical diagnostics, it can be used to predict prognosis or survival rates depending on treatment options. And in economics it can help me understand whether a policy is effective or not.

So what do you think? Should I play golf today? The sum of my random forest decision trees says... yes.?I'll see you out on the course.