Are you wondering whether random forest models can handle missing data? Or maybe you are looking for specific implementations of random forest models that are able to handle missing data? Well either way, you are in the right place! In this article, we tell you everything you need to know about random forests and missing data.
We start out by discussing whether random forest models are able to handle missing data. We also discuss how common it is for implementations of random forest models to be able to handle missing data natively. After that, we discuss how random forest models handle missing data. Finally, we provide examples of common implementations of random forest models and discuss whether they are able to handle missing data.
Can random forest models handle missing data?
Can random forest models handle missing data? Yes, many implementations of random forest models are naturally able to handle missing data. This is a nice quality that makes random forests stand out when compared to other machine learning models, as many machine learning models can not handle missing data natively. When we say that random forest models can handle missing values natively, we mean that they can intelligently use information on the missing values to contribute to the model rather than applying a pre-processing hack to do something like drop all rows of the dataset with missing values.
That being said, just because it is possible to create a random forest model that handles missing data natively, that does not mean that all implementations of random forest models are created in a way that accounts for missing data. There may be some libraries that are not implemented in a way that naturally accommodates datasets that have missing values in them.
How do random forests handle missing data?
How do random forest models handle missing data? There are a number of different ways that missing values can be handled in random forests. Here are a few examples of common methods that can be used.
- Use surrogate splits. One common way to handle missing values in decision trees and random forests is to use surrogate splits. What does it mean to use a surrogate split? This means that when the value of one variable is missing, the model looks to another variable that is related to the missing variable and makes a split based on that variable instead.
- Push all missing values to one side of each split. Another common way to handle missing values in random forests is to look at all the observations that have missing values for the variable you are splitting on and move them all to one side (or the other) in that split. You can decide which side to move these values to based on the same types of metrics that are used to determine where to split the data that does not have missing values.
Examples of random forest models that can handle missing data
What are some examples of implementations of random forest models that are able to handle missing data? In this section, we will discuss some common implementations of random forest models and discuss whether they are able to handle missing data natively.
- Scikit Learn. Scikit Learn is a common Python library that contains implementations of many different machine learning models. For a long time, the implementation of random forests that was used in Scikit Learn did not support missing values even though the implementation on individual decision trees did. That being said, that is no longer the case. As of version 1.4.0, the implementation of random forests that is provided in Scikit Learn does support missing values.
- Hyperparameter tuning for random forests
- Number of trees in random forests
- Max depth in random forests
- Mtry in random forests
- When to use random forests
- Random forest overfitting