Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

ModelFox makes it easy to train, deploy, and monitor machine learning models.

License

NotificationsYou must be signed in to change notification settings

modelfoxdotdev/modelfox

Repository files navigation

ModelFox makes it easy to train, deploy, and monitor machine learning models.

Train a model from a CSV file on the command line. Make predictions from Elixir, Go, JavaScript, PHP, Python, Ruby, or Rust. Learn about your models and monitor them in production from your browser.

Follow @modelfoxdotdev on Twitter

ModelFox

Discord

ModelFox makes it easy to train, deploy, and monitor machine learning models.

  • Runmodelfox train to train a model from a CSV file on the command line.
  • Make predictions with libraries forElixir,Go,JavaScript,PHP,Python,Ruby, andRust.
  • Runmodelfox app to learn more about your models and monitor them in production.

Install

You can install the modelfox CLI by either downloading the binary from thelatest github release, or by building from source.

Train

Train a machine learning model by runningmodelfox train with the path to a CSV file and the name of the column you want to predict.

$ modelfox train --file heart_disease.csv --target diagnosis --output heart_disease.modelfox✅ Loading data.✅ Computing features.🚂 Training model 1 of 8.[==========================================>                         ]

The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a.modelfox file. If you want more control, you can provide a config file.

Predict

Make predictions with libraries forElixir,Go,JavaScript,PHP,Python,Ruby, andRust.

letmodelfox=require("@modelfoxdotdev/modelfox")letmodel=newmodelfox.Model("./heart_disease.modelfox")letinput={age:63,gender:"male",// ...}letoutput=model.predict(input)console.log(output)
{className:'Negative',probability:0.9381780624389648}

Inspect

Runmodelfox app, open your browser tohttp://localhost:8080, and upload the model you trained.

  • View stats and metrics.
  • Tune your model to get the best performance.
  • Make example predictions and get detailed explanations.

report

tune

Monitor

Once your model is deployed, make sure that it performs as well in production as it did in training. Opt in to logging by callinglogPrediction.

// Log the prediction.model.logPrediction({identifier:"6c955d4f-be61-4ca7-bba9-8fe32d03f801",input,options,output,})

Later on, if you find out the true value for a prediction, calllogTrueValue.

// Later on, if we get an official diagnosis for the patient, log the true value.model.logTrueValue({identifier:"6c955d4f-be61-4ca7-bba9-8fe32d03f801",trueValue:"Positive",})

Now you can:

  • Look up any prediction by its identifier and get a detailed explanation.
  • Get alerts if your data drifts or metrics dip.
  • Track production accuracy, precision, recall, etc.

predictions

drift

metrics

Building from Source

This repository is a Cargo workspace, and does not require anything other than the latest nightly Rust toolchain to get started with.

  1. InstallRust on Linux, macOS, or Windows.
  2. Clone this repo andcd into it.
  3. Runcargo run to run a debug build of the CLI.

If you are working on the app, runscripts/app/dev. This rebuilds and reruns the CLI with theapp subcommand as you make changes.

To install all dependencies necessary to work on the language libraries and build releases, installNix withflake support, then runnix develop or set updirenv.

If you want to submit a pull request, please runscripts/fmt andscripts/check at the root of the repository to confirm that your changes are formatted correctly and do not have any errors.

License

All of this repository is MIT licensed, except for thecrates/app directory, which is source available and free to use for testing, but requires a paid license to use in production.


[8]ページ先頭

©2009-2025 Movatter.jp