Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

ML hyperparameters tuning and features selection, using evolutionary algorithms.

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

All Relevant Feature Selection

Leave One Feature Out Importance

Toolbox of models, callbacks, and datasets for AI/ML researchers.

Scikit-Learn API wrapper for Keras.

A library of extension and helper modules for Python's data analysis and machine learning libraries.

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

AssemblyAI's Official Python SDK

🐦 Quickly annotate data from the comfort of your Jupyter notebook

scikit-learn: machine learning in Python

You like pytorch? You like micrograd? You love tinygrad! ❤️

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

pca: A Python Package for Principal Component Analysis.

hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be …

Adding coherence to the SKLearn pipeline

Industry-strength Computer Vision workflows with Keras

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

Adala: Autonomous DAta (Labeling) Agent framework

Fast, high-quality forecasts on relational and multivariate time-series data powered by new feature learning algorithms and automated ML.

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

ONNXMLTools enables conversion of models to ONNX

Convert scikit-learn models and pipelines to ONNX

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports comp…

A game theoretic approach to explain the output of any machine learning model.

Visual analysis and diagnostic tools to facilitate machine learning model selection.

A hyperparameter optimization framework

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models