sjwhitworth/golearn

★ 9,444GoAudience · developerComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((repo))
    What it does
      ML library for Go
      Batteries included
      Scikit-learn pattern
    Core concepts
      Instances data table
      Fit and Predict
      Algorithm swapping
    Built-in helpers
      Train-test split
      Cross-validation
      Accuracy metrics
    Audience
      Go developers
      Python ML switchers
      Beginners in Go

mindmap root((repo)) What it does ML library for Go Batteries included Scikit-learn pattern Core concepts Instances data table Fit and Predict Algorithm swapping Built-in helpers Train-test split Cross-validation Accuracy metrics Audience Go developers Python ML switchers Beginners in Go

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Things people build with this

USE CASE 1

Train and evaluate a classification model in Go without switching to Python, using the familiar Fit and Predict calls.

USE CASE 2

Load a CSV dataset, split it into training and test sets, and print accuracy metrics using built-in GoLearn helpers.

USE CASE 3

Swap different machine learning algorithms in and out of a Go application with minimal code changes.

Tech stack

Getting it running

Difficulty · easy Time to first run · 30min

In plain English

GoLearn is a machine learning library written in Go, a programming language developed at Google. The library is described as "batteries included," meaning it ships with most of what a developer needs to start building machine learning applications without hunting down separate packages. The focus is on keeping things simple while still allowing customization. At the core of GoLearn is a concept called Instances, which act like spreadsheets or data tables. You load your data into an Instances object and then pass it to learning algorithms. The library follows the same Fit and Predict pattern made popular by scikit-learn, a well-known Python machine learning toolkit. If you know that pattern, you can swap different algorithms in and out with minimal changes to your code. GoLearn includes built-in helpers for common tasks like splitting your data into training and testing sets, and running cross-validation, which is a technique for checking how well a model generalizes to new data. The repository ships with practical examples, including a working classifier for the classic Iris flower dataset, which is a standard benchmark used to test machine learning code. The included example code shows the full workflow: load a CSV file, split the data, train a classifier, run predictions, and print accuracy metrics. That walkthrough covers most of the steps a beginner would need to understand. Documentation is available in English, Simplified Chinese, and Traditional Chinese. The project appears to be in slower maintenance mode based on the README, which mentions active development and encourages community feedback, though the tone and tooling references suggest the codebase is several years old. It remains a notable option for developers who prefer to work in Go rather than Python for data-related work.

Copy-paste prompts

Prompt 1

Show me how to load a CSV file in GoLearn, split it into train and test sets, train a KNN classifier, and print the accuracy score.

Prompt 2

I know scikit-learn in Python. Map a typical RandomForest classification workflow onto GoLearn equivalents using the same Fit and Predict pattern.

Prompt 3

Implement k-fold cross-validation on a dataset using GoLearn and print the average accuracy across all folds.

Prompt 4

Help me adapt the GoLearn Iris flower example to classify my own CSV dataset that has different column names and more features.

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