Reverse Dependencies of lightgbm

The following projects have a declared dependency on lightgbm:

• ForestDiffusion — Generating and Imputing Tabular Data via Diffusion and Flow XGBoost Models
• freq-mob — Monotonic Optimal Binning for Frequency Models
• freqtrade — Freqtrade - Crypto Trading Bot
• functime — Time-series machine learning at scale.
• funky-ml — Automated ML by GD-Singh
• future-sales-prediction-2024 — A package for feature extraction, hyperopt, and validation schemas
• g-batch-prediction-pipeline — no summary
• g-training-pipeline — no summary
• galileo-forecast — Thompson Sampling using bootstrap sampling
• gamma-facet — Human-explainable AI.
• gargaml — A personal ML lib
• gators — Model building and model scoring library
• gbm-autosplit — LightGBM/XGBoost interface which tunes n_estimator by splitting data, then refit with entire data
• gbt — A gradient boosted tree library with automatic feature engineering.
• gdp-time-series — no summary
• gecs — LightGBM Classifier with integrated bayesian hyperparameter optimization
• genetic-optimization — Genetic Optimization package
• GML — AUTO Machine Learning & AUTO Feature Engineering with many powerful tools.
• google-vizier — Open Source Vizier: Distributed service framework for blackbox optimization and research.
• google-vizier-dev — Open Source Vizier: Distributed service framework for blackbox optimization and research.
• gps-building-blocks — Modules and tools useful for use with advanced data solutions on Google Ads, Google Marketing Platform and Google Cloud.
• grape-model — GRAPE makes it easy to fit a regression model with hyperparameter optimization.
• GuangBEAT — BEAT
• GuangTestBeat — This package contains several methods for calculating Conditional Average Treatment Effects
• gumly — Gumly
• hb-mltools — A platform for rapid development of Machine Learning algorithms.
• hgboost — hgboost is a python package for hyperparameter optimization for xgboost, catboost and lightboost for both classification and regression tasks.
• hipe4ml — Minimal heavy ion physics environment for Machine Learning
• hipe4ml-converter — Minimal heavy ion physics environment for Machine Learning
• hotpp-benchmark — Evaluate generative event sequence models on the long horizon prediction task.
• house-prices — no summary
• houseprices2023xx — Using Machine Learning to predict the SalePrice of properties
• hpsklearn-compneurobilbao — Hyperparameter Optimization for sklearn, compneurobilbaolab unofficial version.
• hummingbird-ml — Convert trained traditional machine learning models into tensor computations
• hybrid-model-for-russian-sentiment-analysis — Hybrid Model for detecting sensitive content in textual Russian news feeds
• hypergbm — A full pipeline AutoML tool integrated various GBM models
• hyperimpute — A library for NaNs and nulls
• hypermax — Better, faster hyperparameter optimization by mixing the best of humans and machines.
• hypernets — An General Automated Machine Learning Framework
• hyperopt — Distributed Asynchronous Hyperparameter Optimization
• hypertrain — Hypertrain Package
• hypper — Hypergraph-based data mining tool for binary classification.
• imlightgbm — LightGBM for label-imbalanced data with focal and weighted loss function
• imputepy — Impute missing values using Lightgbm
• insolver — Insolver is low-code machine learning library, initially created for the insurance industry.
• InsurAutoML — Automated Machine Learning/AutoML pipeline.
• interpret-community — Microsoft Interpret Extensions SDK for Python
• interpret-image — Microsoft Interpret Image SDK for Python
• interpret-recommenders — Microsoft Interpret Recommenders SDK for Python
• interpret-text — Microsoft Interpret Text SDK for Python
• interpret-vision — Microsoft Interpret Vision SDK for Python
• iotfunctions — Open source component of the Maximo Asset Manager pipeline
• ir-axioms — Intuitive interface to many IR axioms.
• itlubber-automl — https://zhuanlan.zhihu.com/p/447307569
• iWork — description
• jamspy — A HTTP & gRPC client for J.A.M.S - Just Another Model Server in Python
• JLpyUtils — General utilities to streamline data science and machine learning routines in python
• joeutil — ADP Utils
• joplen — Implementation of Joint Optimization of Piecewise Linear Ensembles (JOPLEn).
• jquants-ml — jquants-ml is a python library for machine learning with japanese stock trade using J-Quants on Python 3.8 and above.
• jupyter-quant — Jupyter quant research environment.
• kaggle-autolgb — tune with optuna and model LightGBM
• Kaggler — Code for Kaggle Data Science Competitions.
• kaitian — High-level machine learning library.
• katonic — A modern, enterprise-ready MLOps Python SDK
• kozmoserver-lightgbm — LightGBM runtime for KozmoServer
• kts — A framework for fast and interactive conducting machine learning experiments on tabular data
• lale — Library for Semi-Automated Data Science
• lazyauto — A python package for analysis and model development.
• lazypredict — Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
• lazypredict-nightly — [Updated] Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
• LazyProphet — Time series forecasting with LightGBM
• lazytransform — Clean your data using a scikit-learn transformer in a single line of code
• learnware — The learnware package supports the submission, usability testing, organization, identification, deployment, and reuse of learnware.
• lestari-classifier — An ensemble classifier that combines multiple base models using learned weights
• lgbm-to-code — Convert a trained LGBM instance into conditionals that return the same output as a predict function. Supports javascript, python and C++.
• lgbm2vhdl — Translation of LightGBM model to VHDL
• lianyhaii — A package to win data competition
• lightautoml — Fast and customizable framework for automatic ML model creation (AutoML)
• lightautoml-gpu — Fast and customizable framework for automatic ML model creation (AutoML)
• lightgbm-callbacks — A collection of LightGBM callbacks.
• lightgbm-embedding — Feature embeddings with LightGBM
• lightgbm-feature-importance-evaluator-zhoumath — Evaluate feature importance for LightGBM models using various methods.
• lightgbm-ray — A Ray backend for distributed LightGBM
• lightgbm-tools — LightGM Tools
• lightgbmlss — LightGBMLSS - An extension of LightGBM to probabilistic modelling.
• LightGBMwithBayesOpt — A Python toolkit of light gbm with bayesian optimizer.
• lightwood — Lightwood is Legos for Machine Learning.
• linkedlabs — Get Similar customers (or rows) in data using DNA Matching Algorithms and Artificial Intelligence on your data!
• lmsleepdata — a python analyse tool for LM Data Recorder data
• LnT-HR-AI — Data analysis for Attrition predictions
• lofo-importance — Leave One Feature Out Importance
• lohrasb — This versatile tool streamlines hyperparameter optimization in machine learning workflows.It supports a wide range of search methods, from GridSearchCV and RandomizedSearchCVto advanced techniques like OptunaSearchCV, Ray Tune, and Scikit-Learn Tune.Designed to enhance model performance and efficiency, it's suitable for tasks of any scale.
• lokeshk — A package to install a collection of ML packages
• LUBEAT — This package contains several methods for calculating Conditional Average Treatment Effects
• lucifer-ml — Automated ML by d4rk-lucif3r
• luntaiDs — Make Data Scientist life Easier Tool
• LZBEAT — This package contains several methods for calculating Conditional Average Treatment Effects
• madcat — # MadCat
• mAdvisor — An automated AI/ML solution from Marlabs