Reverse Dependencies of lightgbm

The following projects have a declared dependency on lightgbm:

• Accuinsight — Model life cycle and monitoring library in Accuinsight+
• accutuning-helpers — no summary
• acv-dev — ACV is a library that provides robust and accurate explanations for machine learning models or data
• acv-exp — ACV is a library that provides robust and accurate explanations for machine learning models or data
• adbench — Python package of ADBench
• adversarial-robustness-toolbox — Toolbox for adversarial machine learning.
• aglite-test.tabular — AutoML for Image, Text, and Tabular Data
• aideml — Autonomous AI for Data Science and Machine Learning
• aif360 — IBM AI Fairness 360
• aif360-fork2 — IBM AI Fairness 360
• aisimplekit — Simple lib for various machine learning and AI tasks.
• ale-uy — Tool to perform data cleaning, modeling and visualization in a simple way.
• algo-auto-ml — AutoML library for binary classification and regression tasks
• alhazen-py — Python version of the debugging tool Alhazen
• alpha-automl — Alpha-AutoML: NYU's AutoML System
• alphaml — Build a CLETE Binary Classification Model
• ames-model — Trained regression model for the ames dataset
• AMLpp — Wrapper for ml library
• Amplo — Fully automated end to end machine learning pipeline
• anai-opensource — Automated ML
• apollo-lunar — A Python SDK/CLI for Lunar API
• arfs — All Relevant Feature Selection and Maximal Relevant minimal redundancy FS
• atom-ml — A Python package for fast exploration of machine learning pipelines
• attendance-model — An end to end machine learning app to predict canteens attendance 2-3 weeks ahead in Nantes Metropole
• auger.ai.predict — Auger ML predict python and command line interface
• auto-feature-engineering — A package for automatically generating features
• auto_ml — Automated machine learning for production and analytics
• auto-ml-cl — Auto machine learning with scikit-learn and TensorFlow framework.
• autobmt — a modeling tool that automatically builds scorecards and tree models.
• autoboost — A thin wrapper for step-wise parameter optimization of boosting algorithms.
• AutoClassifierRegressor — Tools for getting analysis of all classifiers and regressors
• autodl-gpu — Automatic Deep Learning, towards fully automated multi-label classification for image, video, text, speech, tabular data.
• autoemulate — An emulator platform for Digital Twins
• AutoEnsembler — This AutoEnsembler helps you to find the best Ensemble model for you
• AutoFeatSelect — Automated Feature Selection & Feature Importance Calculation Framework
• autogl — AutoML tools for graph-structure dataset
• autogluon.tabular — Fast and Accurate ML in 3 Lines of Code
• autogluon-tonyhu-test.tabular — AutoML for Image, Text, and Tabular Data
• autolgbm — autolgbm: tuning lightgbm with optuna
• autom8 — Python AutoML library
• automate-ML — A python module to solve machine learning problems in a mechanized way. The repository is able to preprocess the data and output the results in numerical as well as in the graphical form.
• automl — Automated machine learning for production and analytics
• automl-alex — State-of-the art Automated Machine Learning python library for Tabular Data
• automl-client-core-nativeclient — AutoML native client implementation
• automl-tools — automl_tools
• automlkiller — Auto machine learning, deep learning library in Python.
• autonon — Organon Automated ML Platform
• autopeptideml — AutoML system for building trustworthy peptide bioactivity predictors
• autoPyTorch — Auto-PyTorch searches neural architectures using smac
• autotonne — Auto machine learning, deep learning library in Python.
• autotraino — AutoML for Tabular datasets.
• autotreemodel — auto build a tree model
• AutoTS — Automated Time Series Forecasting
• autoviml — Automatically Build Variant Interpretable ML models fast - now with CatBoost!
• autowoe — Library for automatic interpretable model building (Whitebox AutoML)
• avicenna — AVICENNA: Semantic Debugging
• aydin — Aydin - Denoising but chill
• azureml-automl-runtime — Contains the ML and non-Azure specific common code associated with running AutoML for public use.
• azureml-contrib-datadrift — Azure Machine Learning datadrift
• azureml-datadrift — Contains functionality for data drift detection for various datasets used in machine learning.
• azureml-designer-classic-modules — A variety of modules for data processing, model training, inferencing and evaluation.
• azureml-train-automl-runtime — Used for automatically finding the best machine learning model and its parameters.
• azureml-training-tabular — Contains ML models, featurizers and scoring code which can either be used with AutoML or standalone.
• bad-phylo — Tool for the estimation of the difficulty of phylogenetic placements
• bamboos — Wrapper Functions for pandas, numpy, and scikit learn
• bambu-qsar — bambu (bioassays model builder) is CLI tool to build QSAR models based on PubChem BioAssays datasets
• BAMT — data modeling and analysis tool based on Bayesian networks
• bamt-light — data modeling and analysis tool based on Bayesian networks
• bartbroere-eland — [Development fork!] Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
• batch-prediction-pipeline — no summary
• batch-prediction-pipeline-self — no summary
• bayte — Bayesian target encoding with scikit-learn and scipy
• Beat-ML1 — This package contains several methods for calculating Conditional Average Treatment Effects
• BEAT-TEST — This package contains several methods for calculating Conditional Average Treatment Effects
• BEATAALU — This package contains several methods for calculating Conditional Average Treatment Effects
• bigfeat — Automated feature engineering library
• blml1 — blml1
• blobcity — Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
• bofire — no summary
• bokbokbok — Custom Losses and Metrics for XGBoost, LightGBM, CatBoost
• boost-loss — Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost
• booster-wrappers — Booster Wrappers
• brain-pred-toolbox — The Brain Predictability toolbox (BPt) is a Python based machine learning library designed to work with a range of neuroimaging data. Warning: Not actively maintained as of 11/30/22.
• brainless — Automated Machine Learning for production and analytics
• brebsML — Toutes les librairies que nous utiliseront pour ce comité
• bunruija — A text classification toolkit
• bvpTune — Library for fine tuning the numerical settings of boundary value problem solvers
• cat-spend-training-pipeline — no summary
• causalml — Python Package for Uplift Modeling and Causal Inference with Machine Learning Algorithms
• cga2m-plus — CGA2M_plus is a Python package of Constraint GA2M plus(CGA2M+). CGA2M+ is a model that improves GA2M in two respects: interpretability and accuracy.
• chady — A package for ML libraries
• changtianml — no summary
• chembl-regressor — A ChEMBL biological activity regression model
• cherrypick — Some tools to help the process of feature selection
• china — description
• chrisrichardmiles — Helpful tools for machine learning and coding.
• ciclops — Pipeline for building clinical outcome prediction models on training dataset and transfer learning on validation datasets.
• cleen — Unveiling Insights Through Automated Feature Range Analysis.
• climaticai — climaticai is a library that builds, optimizes, and evaluates machine learning pipelines
• climl — no summary