Reverse Dependencies of lightgbm
The following projects have a declared dependency on lightgbm:
- hipe4ml-converter — Minimal heavy ion physics environment for Machine Learning
- house-prices — no summary
- houseprices2023xx — Using Machine Learning to predict the SalePrice of properties
- hummingbird-ml — no summary
- 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.
- HyPSTER — HyPSTER is a brand new Python package that helps you find compact and accurate ML Pipelines while staying light and efficient
- 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
- JLpyUtils — General utilities to streamline data science and machine learning routines in python
- joeutil — ADP Utils
- jquants-ml — jquants-ml is a python library for machine learning with japanese stock trade using J-Quants on Python 3.8 and above.
- kaggle-autolgb — tune with optuna and model LightGBM
- Kaggler — Code for Kaggle Data Science Competitions.
- katonic — A modern, enterprise-ready MLOps Python SDK
- kts — A framework for fast and interactive conducting machine learning experiments on tabular data
- kyuml — Personal library
- 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.
- 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-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.
- 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
- mallu — Package for easier Machine Learning Workflow.
- mangoml — Simple Machine Learning library
- matsim-tools — MATSim Agent-Based Transportation Simulation Framework - official python tools
- mbtr — Multivariate Boosted Trees Regressor package
- mercs-mixed — MERCS: Multi-Directional Ensembles of Regression and Classification treeS
- metaflow-helper — Convenience utilities for common machine learning tasks on Metaflow
- metats — Meta-Learning for Time Series Forecasting
- miceForest — Missing Value Imputation using LightGBM
- Microsoft-AI-Azure-Utility-Samples — Utility Samples for AI Solutions
- mindware — MindWare: Towards Efficient AutoML System.
- mip-training-pipeline — no summary
- ML-Classification-model-selector-Basavaraj100 — It select best classfication model
- ml-init — Install the main ML libraries
- ml-investment — Machine learning tools for investment
- ML-Navigator — ML-Navigator is a tutorial-based Machine Learning framework. The main component of ML-Navigator is the flow. A flow is a collection of compact methods/functions that can be stuck together with guidance texts.
- ml-recsys-tools — Tools for recommendation systems development
- ml2json — A safe, transparent way to share and deploy scikit-learn models.
- ml4pd — ML4PD - an open-source libray for building Aspen-like process models via machine learning.
- mlagility — MLAgility Benchmark and Tools
- mldrift — Data drift by ML, for ML.
- mlduct — A personal framework for Machine Learning Pipelines.
- mlearner — Machine Learning Library Extensions
- mleko — ML-Ekosystem
- mlem — Version and deploy your models following GitOps principles
- mlexec — The mlexec package is used to run scikit-learn type models with high abstraction.
- mlflowgo — no summary
- mlforecast — Scalable machine learning based time series forecasting
- MLimputer — MLimputer - Null Imputation Framework for Supervised Machine Learning
- mlinsights — Extends the list of supported operators in onnx reference implementation and onnxruntime, or implements faster versions in C++.
- mlkits — Common tools and training models for machine learning.
- mlmachine — Accelerate machine learning experimentation
- mlmodels — Generic model API, Model Zoo in Tensorflow, Keras, Pytorch, Hyperparamter search
- mlops_batch_prediction_pipeline — no summary
- mlops_training_pipeline — no summary
- mlpl — A data science pipeline tool to speed up data science life cycle.
- mlprodict — Python Runtime for ONNX models, other helpers to convert machine learned models in C++.
- mlpype-lightgbm — no summary
- mlserver-lightgbm — LightGBM runtime for MLServer