ahmedbesbes/playground
A Streamlit application to play with machine learning models directly from the browser
This tool helps data science practitioners quickly experiment with different machine learning models directly in a web browser. You provide a dataset and configure various model settings, and it displays the model's decision boundary, performance metrics like accuracy and F1 score, training time, and a Python script to reproduce the results. It's designed for data scientists and analysts who want to build intuition about how classical machine learning models behave with different data and parameters.
No commits in the last 6 months.
Use this if you want to understand how different classical machine learning models work by visually inspecting their decision boundaries and comparing performance metrics and training times.
Not ideal if you need to train models on custom, high-dimensional datasets or deploy models for production use cases.
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83
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26
Language
Python
License
MIT
Category
Last pushed
Feb 24, 2022
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