Random Forests in Julia



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CART-based random forest implementation in Julia.

This package supports:

  • Classification model
  • Regression model
  • Out-of-bag (OOB) error
  • Feature importances
  • Various configurable parameters

Please be aware that this package is not yet fully examined implementation. You can use it at your own risk. And your bug report or suggestion is welcome!


Here you can try overview APIs available from the RandomForests module.


using RDatasets
using RandomForests

# classification
iris = dataset("datasets", "iris")
rf = RandomForestClassifier(n_estimators=100, max_features=:sqrt)
fit(rf, iris[1:4], iris[:Species])
@show predict(rf, iris[1:4])
@show oob_error(rf)
@show feature_importances(rf)

# regression
boston = dataset("MASS", "boston")
rf = RandomForestRegressor(n_estimators=5)
fit(rf, boston[1:13], boston[:MedV])
@show predict(rf, boston[1:13])



There are two separate models available in this package - classification and regression. Each model has its own constructor which is trained by applying the fit method. You can configure these constructors with some keyword arguments listed below:

                        max_features::Union(Integer, FloatingPoint, Symbol)=:sqrt,
                       max_features::Union(Integer, FloatingPoint, Symbol)=:third,
  • n_estimators: the number of weak estimators
  • max_features: the number of candidate features at each split
    • if Integer is given, the fixed number of features are used
    • if FloatingPoint is given, the proportion of given value (0.0, 1.0] are used
    • if Symbol is given, the number of candidate features is decided by a strategy
      • :sqrt: ifloor(sqrt(n_features))
      • :third: div(n_features, 3)
  • max_depth: the maximum depth of each tree
    • the default argument nothing means there is no limitation of the maximum depth
  • min_samples_split: the minimum number of sub-samples to try to split a node
  • criterion: the criterion of impurity measure (classification only)
    • :gini: Gini index
    • :entropy: Cross entropy

RandomForestRegressor always uses the mean squared error for its impurity measure. At the current moment, there is no configurable criteria for regression model.

Learning / Prediction

Once you create a model, you can easily fit the model using the fit method:

rf = RandomForestClassifier()
fit(rf, x, y)

Here the fit methods takes three arguments:

  • rf: the configured model of random forest (RandomForestClassifier or RandomForestRegressor)
  • x: the explanatory variables (AbstractMatrix or DataFrame)
  • y: the response variable (AbstractVector)

Each column of x is a feature of the input data and each row is an individual sample. Each element of y is an output corresponding a row of x, so the number of row of x and the length of y should match. Note that even though the DataFrame object is directly applicable to the fit method, applying a matrix is a much more efficient way to learn quickly.

The prediction using the fitted model is also easy. You can apply the new data to the predict method:

predict(rf, new_x)

This returns a vector of predicted values.


The fitted model includes useful information calculated while learning.

  • oob_error(rf): the out-of-bag error, which is known as a good estimator of generalization error
  • feature_importances(rf): relative importances of each explanatory variable

The feature importances are normalized values such that the sum of the importances is one.

Limitations (and ToDo list)

  • no parallelism

Related package

  • DecisionTree.jl
    • DecisionTree.jl is based on the ID3 (Iterative Dichotomiser 3) algorithm while RandomForests.jl uses CART (Classification And Regression Tree).


The algorithm and interface are highly inspired by those of scikit-learn.

First Commit


Last Touched

5 months ago


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