Implementation in Julia

Random forests are available in the Julia-registered packages from Kenta Sato:

Pkg.update() Pkg.add("RandomForests") 

This is a CART-based random forest implementation in Julia. This package supports:

• Classification models

• Regression models

• Out-of-bag (OOB) errors

• Feature importances

• Various configurable parameters

There are two separate models available in this package:

• Classification

• Regression

Each model has its own constructor that is trained by applying the fit method. We can configure these constructors with some keyword arguments listed as follows:

RandomForestClassifier(;n_estimators::Int=10, 
                        max_features::Union(Integer, FloatingPoint, Symbol)=:sqrt, 
                        max_depth=nothing, 
                        min_samples_split::Int=2, 
                        criterion::Symbol=:gini) 

This one is for the classification:

RandomForestRegressor(;n_estimators::Int=10, 
                       max_features...