a machine learning tool that allows to train, test and use models without writing code
Project description
igel
A machine learning tool that allows to train/fit, test and use models without writing code
Free software: MIT license
Documentation: https://igel.readthedocs.io.
Intro
igel is built on top of scikit-learn. It provides a simple way to use machine learning without writing a single line of code
All you need is a yaml file, where you need to describe what you are trying to do. That’s it!
Quick Start
First step is to provide a yaml file:
# model definition
model:
type: regression
algorithm: forest
# target you want to predict
target:
- GPA
In the example above, we declare that we have a regression problem and we want to use the random forest model to solve it. Furthermore, the target we want to predict is GPA (since I’m using this simple dataset: https://www.kaggle.com/luddarell/101-simple-linear-regressioncsv)
Run this command in Terminal, where you provide the path to your dataset and the path to the yaml file
$ igel fit --data_path 'path_to_your_csv_dataset' --model_definition_file 'path_to_your_yaml_file'
That’s it. Your “trained” model can be now found in the model_results folder (automatically created for you in your current working directory). Furthermore, a description can be found in the description.json file inside the model_results folder.
Examples
Check the examples folder, where you can use the csv data to run a simple example from terminal
TODO
add option as arguments to the models
add multiple file support
History
0.0.3 (2020-08-30)
First functional package
0.0.1 (2020-08-27)
First release on PyPI.
Project details
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