goscore Alternatives - Go Machine Learning

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Programming language: Go

Tags: Machine Learning

goscore alternatives and similar packages

Based on the "Machine Learning" category

GoLearn

9.7 7.1 goscore VS GoLearn

General Machine Learning library for Go.
Gorgonia

9.1 8.3 goscore VS Gorgonia

Gorgonia is a library that helps facilitate machine learning in Go.
gosseract

8.1 3.0 goscore VS gosseract

Go package for OCR (Optical Character Recognition), by using Tesseract C++ library.

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tfgo

8.1 4.1 goscore VS tfgo

Easy to use Tensorflow bindings: simplifies the usage of the official Tensorflow Go bindings. Define computational graphs in Go, load and execute models trained in Python.
goml

7.9 0.0 goscore VS goml

On-line Machine Learning in Go
gorse

7.7 7.0 goscore VS gorse

A High Performance Recommender System Package based on Collaborative Filtering for Go.
bayesian

7.4 0.0 goscore VS bayesian

Naive Bayesian Classification for Golang.
gago

7.3 2.1 goscore VS gago

Multi-population, flexible, parallel genetic algorithm.
CloudForest

7.3 0.0 goscore VS CloudForest

Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
gobrain

6.8 0.1 goscore VS gobrain

Neural Networks written in go
ocrserver

6.4 2.1 goscore VS ocrserver

A simple OCR API server, seriously easy to be deployed by Docker and Heroku.
regommend

6.0 0.0 goscore VS regommend

Recommendation & collaborative filtering engine
go-deep

6.0 1.5 goscore VS go-deep

A feature-rich neural network library in Go.
onnx-go

5.9 3.4 goscore VS onnx-go

Go Interface to Open Neural Network Exchange (ONNX).
sklearn

5.8 3.8 goscore VS sklearn

partial port of scikit_learn to golang
go-galib

5.7 0.0 goscore VS go-galib

Genetic Algorithms library written in Go / golang
goRecommend

5.2 0.0 goscore VS goRecommend

Recommendation Algorithms library written in Go.
shield

5.1 0.0 goscore VS shield

Bayesian text classifier with flexible tokenizers and storage backends for Go
Goptuna

4.8 8.8 goscore VS Goptuna

Bayesian optimization framework for black-box functions written in Go. Everything will be optimized.
go-fann

4.7 0.0 goscore VS go-fann

Go bindings for Fast Artificial Neural Networks(FANN) library.
goga

4.4 0.0 goscore VS goga

Genetic algorithm library for Go.
libsvm

4.2 0.0 goscore VS libsvm

libsvm golang version derived work based on LIBSVM 3.14.
neat

4.2 0.0 goscore VS neat

Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT).
go-pr

3.9 0.0 goscore VS go-pr

Pattern recognition package in Go lang.
neural-go

3.9 0.0 goscore VS neural-go

A multilayer perceptron network implemented in Go, with training via backpropagation.
NEUGO

3.8 0.0 goscore VS NEUGO

NEUGO: Neural Networks in Go
fonet

3.2 0.0 goscore VS fonet

A Deep Neural Network library written in Go.
Varis

3.0 0.0 goscore VS Varis

Golang Neural Network.
go-cluster

2.9 0.0 goscore VS go-cluster

Go implementation of the k-modes and k-prototypes clustering algorithms.
EAGO

2.9 0.0 goscore VS EAGO

EAGO: Evolutionary Algorithms in Go
golinear

2.7 0.5 goscore VS golinear

liblinear bindings for Go
godist

2.6 0.0 goscore VS godist

Various probability distributions, and associated methods.
evoli

2.4 6.0 goscore VS evoli

Genetic Algorithm and Particle Swarm Optimization library.
probab

1.9 0.0 goscore VS probab

Probability distribution functions. Bayesian inference. Written in pure Go.
GoMind

1.4 0.0 goscore VS GoMind

A simplistic Neural Network Library in Go.
randomforest

1.1 1.9 goscore VS randomforest

Easy to use Random Forest library for Go.
mlgo

1.0 0.0 goscore VS mlgo

This project aims to provide minimalistic machine learning algorithms in Go.

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Popular Comparisons

README

Goscore

Go scoring API for Predictive Model Markup Language (PMML).

Currently supports Neural Network, Decision Tree, Random Forest and Gradient Boosted Models

Will be happy to implement new models by demand, or assist with any other issue.

Contact me here or at aschers@gmail.com.

Tutorial - Deploy Machine Learning Models from R Research to Go Production with PMML

Installation

go get github.com/asafschers/goscore

Usage

Random Forest / Gradient Boosted Model

Generate PMML - R

Fetch model from PMML file -

modelXml, _ := ioutil.ReadFile("fixtures/model.pmml")
var model goscore.RandomForest // or goscore.GradientBoostedModel
xml.Unmarshal([]byte(modelXml), &model)

Set features -

features := map[string]interface{}{
  "Sex": "male",
  "Parch": 0,
  "Age": 30,
  "Fare": 9.6875,
  "Pclass": 2,
  "SibSp": 0,
  "Embarked": "Q",
}

Score features by model -

score, _ := model.Score(features, "1") // gbm.score doesn't recieve label

Score faster -

runtime.GOMAXPROCS(runtime.NumCPU()) // use all cores
score, _ := model.ScoreConcurrently(features, "1") // parallelize tree traversing

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/asafschers/goscore. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant](contributor-covenant.org) code of conduct.

License

The gem is available as open source under the terms of the MIT License.

*Note that all licence references and agreements mentioned in the goscore README section above are relevant to that project's source code only.

goscore

Go Scoring API for PMML.