Laion

dataframe-go is a Go library for statistics, machine learning, and data manipulation/exploration. It provides DataFrames, similar to Excel spreadsheets, for data analysis. The package is designed to be lightweight and intuitive. The API is still under development but is production-ready.

causalml is a Python package for uplift modeling and causal inference. It utilizes machine learning algorithms to estimate treatment effects. The tool is designed to help data scientists and researchers. It supports various methods for causal analysis and experimentation.

Chinese-Text-Classification-Pytorch is a toolkit for Chinese text classification using PyTorch. It includes implementations of various models like TextCNN, TextRNN, FastText, and Transformer. The toolkit is designed to be easy to use and ready to deploy for various text classification tasks. It supports character-level input and pre-trained word vectors.

shapiq is a Python library for Shapley interactions and Shapley values in machine learning. It helps explain the output of machine learning models. The library facilitates feature importance analysis and model understanding. It is an open-source tool for machine learning explainability.

Nimfa is a Python module providing various algorithms for nonnegative matrix factorization. It is designed for tasks such as data analysis and feature extraction. Nimfa is distributed under the BSD license. It supports a range of methods for matrix factorization.

photon-ml is a machine learning library built on Apache Spark. It supports training Generalized Linear Models (GLMs) and Generalized Linear Mixed Models (GLMMs). Originally developed by LinkedIn, it is designed for scalable machine learning tasks. photon-ml is suitable for large-scale data analysis and model training.