model-explain 0.4.0

A unified Python package for interpreting and explaining machine learning and deep learning models, including feature attention mechanisms. Supports popular explanation techniques such as LIME, SHAP, Grad-CAM, permutation importance, and saliency maps. Provides a consistent interface for tabular, image, and other data types, enabling model transparency and interpretability.

Model Explain

---

---

model_explain is a Python package for interpreting and explaining machine learning models. It provides a unified interface for popular explanation techniques, including LIME, SHAP, and Grad-CAM and supports a wide range of models and data types.

Key Features

• Unified API for LIME and SHAP explanations
• Model-agnostic: works with any model supporting predict or predict_proba
• Tabular, image, and text data support
• Visualizations for both local and global explanations
• Easy integration with scikit-learn, XGBoost, LightGBM, and more
• Feature importance extraction and plotting
• Interpretability for individual predictions and datasets

Installation

Install from PyPI:

pip install model-explain

Or, if you prefer to clone the repository and install manually:

git clone https://github.com/vaibhav-k/model_explain.git
cd model_explain
pip install .

Quick Start

Tabular Data Example (LIME)

from model_explain.explainers.lime_explainer import lime_explainer

# model: trained scikit-learn model
# X_test: pandas DataFrame of test features

explanation = lime_explainer(model, X_test, instance_index=0)
explanation.show_in_notebook()

Tabular Data Example (SHAP)

import shap
from model_explain.explainers.shap_explainer import shap_explainer

# model: trained machine learning model
# X_test: pandas DataFrame of test features

shap_values = shap_explainer(model, X_test)
shap.summary_plot(shap_values, X_test)

Image Data Example

from model_explain.explainers.grad_cam import GradCAM
import matplotlib.pyplot as plt

# model: your trained CNN model (e.g., from torchvision)
# image: a preprocessed image tensor of shape [1, C, H, W]
# predicted_class: integer index of the predicted class

explainer = GradCAM(model, target_layer_name="layer4")  # specify the last conv layer
heatmap = explainer(image, target_idx=predicted_class)

plt.imshow(heatmap, cmap="jet", alpha=0.5)
plt.title("Grad-CAM Heatmap")
plt.axis("off")
plt.show()

Supported Models

• Scikit-learn models
• XGBoost
• LightGBM
• PyTorch models
• Any model with predict or predict_proba methods

Visualizations

• SHAP summary plot: global feature importance (use plot_feature_importance for custom bar plots)
• LIME explanation plot: local feature contributions (use plot_feature_importance for instance-level contributions)
• Image region importance (Grad-CAM heatmap): highlights spatial regions in images that most influence the model's prediction

Use Cases

• Debugging and validating ML models
• Regulatory compliance and transparency
• Feature selection and engineering
• Enhancing trust in AI systems
• Explaining model predictions to stakeholders

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for details on how to contribute.

License

This project is licensed under the MIT License - see the LICENSE file for details.