Random forest overfitting

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Are you wondering whether random forest models are prone to fitting? In this article we discuss everything you need to know about random forest models and overfitting. We start with a discussion of what overfitting is and how to determine when your model is overfitting. After that we discuss random forests and the likelihood that random forest models will overfit. 

An example of what prediction error on the test data and training data might look like as model complexity increases if a model is overfitting.

What is overfitting?

What is overfitting? Overfitting is a common phenomenon you should look out for any time you are training a machine learning model. Overfitting happens when a model pays too much attention to the specific details of the dataset that it was trained on. Specifically, the model picks up on patterns that are specific to the observations in the training data, but do not generalize to other observations. The model is able to make great predictions on the data it was trained on, but is not able to make good predictions on data it did not see during training. 

Why is overfitting a problem?

Why is overfitting a problem? Overfitting is a problem because machine learning models are generally trained with the intention of making predictions on unseen data. Models that have overfit to their training data set are not able to make good predictions on new data that they did not see during training, so they are not able to make predictions on unseen data. 

How to recognize overfitting?

If you plan to use a machine learning model to make predictions on unseen data, you should always check to make sure that your model is not overfitting to the training data. How do you check whether you model is overfitting to the training data? 

In order to check whether your model is overfitting to the training data you should make sure to split your dataset into a training dataset that is used to train your model and a test dataset that is not touched at all during model training. This way you will have a dataset available that the model did not see at all during training that you can use to assess whether your model is overfitting. 

You should generally allocate around 70% of your data to the training dataset and 30% of your data to the test dataset. Only after you train your model on the training dataset and optimize and hyper parameters you plan to optimize should you use your test dataset. At that point you can use your model to make predictions on both the test data and the training data then compare the performance metrics on the test and training data.

If your model is overfitting to the training data, you will notice that the performance metrics on the training data are much better than the performance metrics on the test data. 

Is overfitting a problem with random forests?

Is overfitting a problem you need to look out for when you are working with random forests? In general, random forests are much less likely to overfit than other models because they are made up of many weak classifiers that are trained completely independently on completely different subsets of the training data. 

Random forests are a great option to spring for if you want to train a quick model that is not likely to overfit. That being said, it is possible that a random forest model might overfit in some cases so you should still make sure to look out for overfitting when you train random forest models. 

How to prevent overfitting in random forests

How do you prevent overfitting in random forest models? And how do you treat the problem of overfitting if it does crop up? Here are some easy ways to prevent overfitting in random forests.  

  • Reduce tree depth. If you do believe that your random forest model is overfitting, the first thing you should do is reduce the depth of the trees in your random forest model. Different implementations of random forest models will have different parameters that control this, but generally there will be a parameter that explicitly controls the number of levels deep a tree can get, the number of splits a tree can have, or the minimum size of the terminal nodes. Reducing model complexity generally ameliorates overfitting problems and reducing tree depth is the easiest way to reduce complexity in random forests. 
  • Reduce the number of variables sampled at each split. You can also reduce the number of variables considered for each split to introduce more randomness into your model. To take a step back, each time a split is created in a tree, a subset of variables is taken and only those variables are considered to be the variable that is split on. If you consider all or most of your variables at each split, your trees may all end up looking the same because the same splits on the same variables are chosen. If you consider a smaller subset of variables at each split, the trees are less likely to look the same because it is unlikely that the same variables were even available for consideration at each split. 
  • Use more data. Finally, you can always try increasing the size of your dataset. Overfitting is more likely to happen when complex models are trained on small datasets so increasing the size of your dataset may help.

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