Metadata-Version: 2.4
Name: model_explain
Version: 0.1.0
Summary: A model explainability toolkit with SHAP, LIME, scoring, summaries, and GUI sandbox
Author: Vaibhav Kulshrestha
Author-email: vaibhav1kulshrestha@gmail.com
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: plotly
Requires-Dist: fairlearn
Requires-Dist: dice-ml
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: shap
Requires-Dist: lime
Requires-Dist: streamlit
Requires-Dist: xgboost
Requires-Dist: joblib
Requires-Dist: matplotlib
Requires-Dist: seaborn
Dynamic: author
Dynamic: author-email
Dynamic: description
Dynamic: description-content-type
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Model Explainability Toolkit

This toolkit provides SHAP and LIME-based explanations for scikit-learn models, along with visualization tools.

## Features
- SHAP and LIME explainers
- Feature importance plots
- Modular design for easy extension

## Example
```python
from model_explain.explainers import shap_explainer
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd
# Load data
data = load_iris()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train model
my_model = RandomForestClassifier()
my_model.fit(X_train, y_train)
# Explain model
shap_explainer.shap_explainer(my_model, X_test)
```

## Usage
See `examples/demo_notebook.ipynb` for a walkthrough.
