Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

ml-utils

ml_utils is a Python package that provides a suite of plotting functions to visualize machine learning models and data. It offers intuitive and customizable plots to aid in model evaluation and data analysis.

Features

  • Model Evaluation Plots:
    • Confusion matrices
    • ROC curves
    • Precision-recall curves
  • Data Visualization:
    • Heatmaps
    • Pair plots
    • Feature importance plots
  • Compatibility:
    • Integrates seamlessly with popular machine learning libraries like scikit-learn and TensorFlow.

Installation

You can install ml_utils using pip:

pip install ml_utils

Metadata

Release files for graph-former 0.0.1a0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for graph-former 0.0.1a0
File Size Uploaded
graph_former-0.0.1a0.tar.gz 4.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for graph-former 0.0.1a0
File Interpreter ABI Platform
graph_former-0.0.1a0-py3-none-any.whl Python 3 none any Details

Total release size: 8.4 kB

Release files / graph_former-0.0.1a0.tar.gz

Download URL graph_former-0.0.1a0.tar.gz
Size 4.3 kB
Tags Source
SHA-256 checksum
How to use checksums
de920189a69155587906ba31f47df1a53b79fdb75e521f515636ec38ef2950d3
BLAKE2b-256 checksum
How to use checksums
36a3115653c5c35b8317d6007c3a08ee86c45b8e8ce9d6ee668931dade156c7b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.0.1 CPython/3.12.8

Release files / graph_former-0.0.1a0-py3-none-any.whl

Download URL graph_former-0.0.1a0-py3-none-any.whl
Size 4.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
535c4f4d7c0c6c53ebc9910f91e4762be4d4f066a4337db65cf95c9a6a0dee5a
BLAKE2b-256 checksum
How to use checksums
01599b2b3f000343006dd21e9728b8e3e8c229a4f053c8c5ff91aa4fc34b7da0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.0.1 CPython/3.12.8

Release history Release notifications | RSS feed

This release

0.0.1a0 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page