Repository Index

Python AutoML for Trading Systems and Sports Betting

• machine-learning
• predictive-analytics
• classification
• regression

Machine Learning in R

• machine-learning
• data-science
• tuning
• cran

Hyper-parameter optimization for sklearn

Hyperparameter Experiments with TensorFlow and Keras

• deep-learning
• keras
• keras-tensorflow
• hyperparameter-optimization

MLBox is a powerful Automated Machine Learning python library.

• machine-learning
• auto-ml
• kaggle
• deep-learning

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

• bayesian-optimization
• bayesian-optimisation
• hyperparameter-optimization
• hyperparameter-tuning

An autoML framework & toolkit for machine learning on graphs.

• graph-neural-networks
• automl
• pytorch
• pytorch-geometric

Use evolutionary algorithms instead of gridsearch in scikit-learn

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

• pipeline
• pipeline-framework
• machine-learning
• deep-learning

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

• python
• pytorch
• machine-learning
• deep-learning

optimization routines for hyperparameter tuning

Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.

• python
• scikit-learn
• machine-learning
• artificial-intelligence

A hyperparameter optimization framework, inspired by Optuna.

• bayesian-optimization
• bandit-algorithms
• evolution-strategies
• blackbox-optimization

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

• svm
• regression
• classification
• machine-learning