Programming language: Go
License: Apache License 2.0
Tags: Machine Learning    
Latest version: v1.1.1

randomforest alternatives and similar packages

Based on the "Machine Learning" category.
Alternatively, view randomforest alternatives based on common mentions on social networks and blogs.

Do you think we are missing an alternative of randomforest or a related project?

Add another 'Machine Learning' Package


GoDoc: https://godoc.org/github.com/malaschitz/randomForest


go test ./... -cover -coverpkg=.  


Random Forest implementation in golang.

Simple Random Forest

    xData := [][]float64{}
    yData := []int{}
    for i := 0; i < 1000; i++ {
        x := []float64{rand.Float64(), rand.Float64(), rand.Float64(), rand.Float64()}
        y := int(x[0] + x[1] + x[2] + x[3])
        xData = append(xData, x)
        yData = append(yData, y)
    forest := randomForest.Forest{}     
    forest.Data = randomforest.ForestData{X: xData, Class: yData}
    fmt.Println("Vote", forest.Vote([]float64{0.1, 0.1, 0.1, 0.1})) 
    fmt.Println("Vote", forest.Vote([]float64{0.9, 0.9, 0.9, 0.9}))

Extremely Randomized Trees


Deep Forest

Deep forest inspired by https://arxiv.org/abs/1705.07366

    dForest := forest.BuildDeepForest()
    dForest.Train(20, 100, 1000) //20 small forest with 100 trees help to build deep forest with 1000 trees

Continuos Random Forest

Continuos Random Forest for data where are still new and new data (forex, wheather, user logs, ...). New data create a new trees and oldest trees are removed.

forest := randomForest.Forest{}
data := []float64{rand.Float64(), rand.Float64()}
res := 1; //result
forest.AddDataRow(data, res, 1000, 10, 2000) 
// AddDataRow : add new row, trim oldest row if there is more than 1000 rows, calculate a new 10 trees, but remove oldest trees if there is more than 2000 trees.

Boruta Algorithm for feature selection

Boruta algorithm was developed as package for language R. It is one of most effective feature selection algorithm. There is paper in Journal of Statistical Software.

Boruta algorithm use random forest for selection important features.

    xData := ... //data
    yData := ... //labels
    selectedFeatures := randomforest.BorutaDefault(xData, yData)
    // or randomforest.BorutaDefault(xData, yData, 100, 20, 0.05, true, true)

In /examples is example with MNIST database. On picture are selected features (495 from 784) from images.

[boruta 05](boruta05.png)