Randomforest: Random forest Implementation in Golang

Test:

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

randomForest

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}
    forest.Train(1000)
    //test
    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

    forest.TrainX(1000)    

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.

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

Author: Malaschitz
Source Code: https://github.com/malaschitz/randomForest 
License: Apache-2.0 License

#machinelearning #go #golang 

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Introduction to Random Forest Algorithm: Functions, Applications & Benefits

Random Forest is a mainstream AI algorithm that has a place with the regulated learning strategy. It might be used for both Classification and Regression issues in ML. It depends on the idea of ensemble learning, which is a cycle of joining numerous classifiers to tackle an intricate issue and to improve the presentation of the model.

As the name proposes, “Random Forest is a classifier that contains different decision trees on various subsets of the given dataset and takes the typical to improve the perceptive precision of that dataset.”

Instead of relying upon one decision tree, the random forest takes the figure from each tree and subject it to the larger part votes of desires, and it predicts the last yield. The more noticeable number of trees in the forest prompts higher exactness and forestalls the issue of overfitting.

**Presumptions for Random Forest **

Since the random forest consolidates various trees to anticipate the class of the dataset, it is conceivable that some choice trees may foresee the right yield, while others may not. Yet, together, all the trees anticipate the right yield. In this way, beneath are two presumptions for a superior random forest classifier:

• There should be some real qualities in the component variable of a dataset with a goal that the classifier can foresee precise outcomes as opposed to a speculated result.
• The forecasts from each tree must have low connections.

#artificial intelligence #random forest #introduction to random forest algorithm #random forest algorithm #algorithm

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Randomforest: Random forest Implementation in Golang

Test:

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

randomForest

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}
    forest.Train(1000)
    //test
    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

    forest.TrainX(1000)    

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.

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

Author: Malaschitz
Source Code: https://github.com/malaschitz/randomForest 
License: Apache-2.0 License

#machinelearning #go #golang 

Random Forest Algorithm in Python from Scratch

Coding the powerful algorithm in python using (mainly) arrays and loops

This article aims to demystify the popular random forest (here and throughout the text —** RF**) algorithm and show its principles by using graphs, code snippets and code outputs.

The full implementation of the RF algorithm written by me in python can be accessed via: https://github.com/Eligijus112/decision-tree-python

I highly encourage anyone who stumbled upon this article to dive deep into the code because the understanding of the code will make any future documentation reading about **RF **much more straightforward and less stressful.

Any suggestions about optimizations are highly encouraged and are welcomed via a pull request on GitHub.

The building blocks of RF are simple decision trees. This article will be much easier to read if the reader is familiar with the concept of a classification decision tree. It is highly recommended to go through the following article before going any further:

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