Metadata-Version: 2.1
Name: xplainit
Version: 0.4
Summary: A library to generate natural language explanations of ML model predictions
Home-page: https://github.com/
Author: Leandre Nash
Author-email: leandrework@gmail.com
License: MIT
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib

# xplainit

[![PyPI version](https://badge.fury.io/py/xplainit.svg)](https://badge.fury.io/py/xplainit)

`xplainit` is an open-source Python library designed to generate natural language explanations for machine learning model predictions. This tool is especially useful for non-technical stakeholders who need to understand how features impact predictions made by models like Random Forest, XGBoost, or other models built using Scikit-learn, TensorFlow, or PyTorch.

## Key Features

- **Natural Language Explanations**: Translates complex model outputs into easy-to-understand narratives.
- **Model Support**: Works with Scikit-learn models and is extensible for TensorFlow, PyTorch, and other machine learning frameworks.
- **Feature Importance Visualization**: Automatically generates feature importance charts.
- **Easy to Use**: Minimal configuration needed to integrate into any data science pipeline.

## Table of Contents

- [Installation](#installation)
- [Quick Start](#quick-start)
- [Supported Models](#supported-models)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)

## Installation

You can install `xplainit` directly from PyPi:

```bash
pip install xplainit
