Getting Started

This documentation provides an overview of the ML Wrappers SDK, which is designed to provide a uniform format for wrapping datasets and models.

Installation

The ML Wrappers SDK can be installed via pip:

pip install ml-wrappers

Supported Models

The ML Wrappers SDK supports the following models:

Scikit-Learn
LightGBM
XGBoost
Catboost
Keras with Tensorflow backend
Pytorch
ONNX (planned for future support)

For more details, please refer to the Supported Models section.

Supported Frameworks

The ML Wrappers SDK supports the following frameworks:

Scikit-Learn
LightGBM
XGBoost
Catboost
Keras with Tensorflow backend
Pytorch
ONNX (planned for future support)

For more details, please refer to the Supported Frameworks section.

Model Wrapping

The ML Wrappers SDK provides a way to wrap models into a uniform format. This is done by either using the predict_proba function, or, if it is not available, the predict function. For more details, please refer to the Model Wrapping section.

Dataset Wrapping

The ML Wrappers SDK provides a way to wrap datasets into a uniform format. This is done using the DatasetWrapper class. For more details, please refer to the Dataset Wrapping section.

License Information

The ML Wrappers SDK is licensed under the MIT License. For more details, please refer to the License Information section.

Support

Support for this project is limited to the resources listed in the Support section.