Are you wondering what the number of trees means in a random forest model? Or maybe you are more interested in learning what range of values you should try for the number of trees? Well either way, you are in the right place! In this article we tell you everything you need to know about the number of trees in a random forest model.
We start out by discussing what the number of trees is and what this parameter controls. After that, we discuss whether it is important to tune the number of trees hyperparameter. Next, we discuss how many trees you should include in a random forest model. Finally, we discuss other random forest parameters that are related to the number of trees.
What is the number of trees in a random forest?
What is the number of trees in a random forest? In order to understand what the number of trees is in a random forest model, we will first talk a little bit about how random forest models work.
A random forest model is an ensemble model that is made up of a collection of simple models called decision trees. It is okay if you are not familiar with exactly what a decision tree is. The most important thing to understand is that a random forest model is made up of many simple models that are trained independently of one another. The predictions from the different models are combined to create the final prediction.
The number of trees parameter in a random forest model determines the number of simple models, or the number of decision trees, that are combined to create the final prediction. If the number of trees is set to 100, then there will be 100 simple models that are trained on the data. After that, the predictions made by each of these models will be aggregated up to create one final prediction.
Is the number of trees an important parameter to tune?
Is the number of trees an important parameter to tune in a random forest model? The short answer to this question is yes, the number of trees is one of the most important parameters that you should tune in a random forest model. Whenever you are taking the time to tune the hyperparameters of a random forest model, you should make sure to include the number of trees in the list of hyperparameters you are tuning.
How many trees should you include in a random forest?
How many trees should you include in your random forest model? In this section, we will give you all of the information that you need to answer that question. First we will discuss the advantages and disadvantages of having a large number of trees in your model. After that, we will talk about the range of values you should consider when you are tuning this hyperparameter.
Advantages of having a large number of trees in a random forest
The main advantage of using a large number of simple models, or decision trees, in your random forest model is that predictive performance tends to increase as the number of trees increases. It is important to note that after a certain point, you start to hit a point of diminishing returns where the scale of the performance gains you see from adding more trees gets smaller and smaller.
The point where you start to see diminishing returns will vary from model to model. If you are building a very simple model with only a few features, you will likely start to see diminishing returns faster than if you are building a very complex model with many features.
Disadvantages of having a large number of trees in a random forest
The disadvantages of having a large number of trees in your random forest relate more to computational efficiency than predictive performance. The more trees you include in your random forest model, the longer it will take to train your model. Slow training times can be particularly problematic when you are training many different models with different combinations of hyperparameters.
The main reason to reduce the number of trees in your random forest model is to reduce the time it takes to train your model. You tend to see diminishing returns in predictive performance as you increase the number of trees in your model, so you need to make a tradeoff between predictive performance and computational performance.
Random forest models that are made with many trees also encode more complexity than random forest models that are made with a small number of trees. That means that random forest models that have many trees may overfit to the dataset they were trained on. For more information on this, check out our article on random forest overfitting.
Range of values to consider for number of trees
What range of values should you consider when you are tuning the number of trees in a random forest? As a general rule of thumb, you should try values ranging from 50 trees to 400 trees. There will be some cases where you will need to use more or less trees than this, but this is a fairly reliable range of values that will cover most cases.
There are a few different factors that will affect the number of trees you will need in your model. Here are some other heuristics to consider when determining how many trees to include in a random forest model.
- You may need more trees if you are using shallow trees. Another parameter you can tune when creating a random forest model is tree depth. This parameter controls how many levels deep a decision tree can be. In general, if you are limiting your model to only include shallow trees then you may need to include more trees. This is because shallow trees are very simple and cannot capture a lot of complexity.
- You may need more trees if you are using more features. If you are building a very simple model that only has a few features in it, then the number of trees you need in your model is likely to be on the lower side. This is simply because there is not a lot of information to capture in the dataset.
Parameters that are similar to the number of trees
Is there a difference between the number of trees and the number of estimators?
The number of estimators is another common parameter that comes up when training a random forest model. Is there a difference between the number of trees and the number of estimators in a random forest model?
The fact is that there is not a difference between the number of trees and the number of estimators in a random forest model. Number of trees and number of estimators are just two different ways to refer to the same concept, which is the number of simple models that are combined to create a random forest model. Different machine learning libraries simply use different names to describe the same parameter.
Related articles
- Hyperparameter tuning for random forests
- Max depth in random forests
- Mtry in random forests
- When to use random forests
- Random forest overfitting
- Missing data in random forests
