RandomForestRegressor in Julia

Specific Domains Machine Learning
8

I gave up on using DecisionTree.jl , And ScikitLearn.jl is adequate in my case:

using ScikitLearn: @sk_import, fit!, predict
@sk_import ensemble: RandomForestRegressor
using ScikitLearn.GridSearch: RandomizedSearchCV
using CSV
using DataFrames


x = CSV.read("x.csv", DataFrames.DataFrame)
x_test = CSV.read("x_test.csv", DataFrames.DataFrame)
y_train = CSV.read("y_train.csv", DataFrames.DataFrame)

x_test = reshape(x_test, 1,length(x_test))

mod = RandomForestRegressor()
param_dist = Dict("n_estimators"=>[50 , 100, 200, 300],
                  "max_depth"=> [3, 5, 6 ,8 , 9 ,10])
model = RandomizedSearchCV(mod, param_dist, n_iter=10, cv=5)

fit!(model, Matrix(x), Matrix(DataFrames.dropmissing(y_train)))

predict(model, x_test)

This works fine for me, But it’s super slow! Much slower than Python.