Reverse Dependencies of lightgbm

The following projects have a declared dependency on lightgbm:

• 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
• MedicalMultitaskModeling — Multitask learning framework for medical data
• mercs-mixed — MERCS: Multi-Directional Ensembles of Regression and Classification treeS
• metaflow-helper — Convenience utilities for common machine learning tasks on Metaflow
• metaforecast — Meta-learning and Data-centric Forecasting
• metats — Meta-Learning for Time Series Forecasting
• miceForest — Multiple Imputation by Chained Equations with LightGBM
• Microsoft-AI-Azure-Utility-Samples — Utility Samples for AI Solutions
• mindware — MindWare: Towards Efficient AutoML System.
• mip-training-pipeline — no summary
• mkyz — MKYZ is a Python library for classification, regression, clustering, association rule learning, dimensionality reduction, bagging, boosting, and stacking.
• 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
• mlcompare — Quickly compare machine learning models across libraries and datasets.
• 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
• MLEssentials — A toolkit to install essential machine learning libraries with one command.
• 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 - Missing Data 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
• mlshell — Ml framework.
• mlsuite — The traditional machine learning analysis based on sklearn package
• mmfunctions — Helper package to be used in conjunction with the Maximo Asset Manager pipeline
• model-monitoring — Model Monitoring
• modeling-tool — the extension of sklearn to help the your modeling code becomes more concise with common useful tool for modeling
• modelLab — A lib for automating model training process of choosing best model that works for you data
• morai — A mortality viewer
• mqboost — Monotonic composite quantile gradient boost regressor
• ms1searchpy — A proteomics search engine for LC-MS1 spectra.
• MultiTrain — MultiTrain allows you to train multiple machine learning algorithms on a dataset all at once to determine the best for that particular use case
• musket-core — The core of Musket ML
• my_krml_24996199 — A package for doing great things!
• myylearn — An General Automated Machine Learning Framework
• namedivider-python — A tool for dividing the Japanese full name into a family name and a given name.
• nanosense — A comprehensive package for solid state nanopore data analysis and visualization.
• naszilla — python framework for NAS algorithms on benchmark search spaces
• nclick — Code for Laziness.
• neptune-lightgbm — Neptune.ai LightGBM integration library
• nessie — Annotation error detection and correction
• nestedhyperboost — A wrapper for conducting Nested Cross-Validation with Bayesian Hyper-Parameter Optimized Gradient Boosting
• neurodecode — Real-time brain signal decoding framework
• nexora — This is an ML project in order to automate ML processes
• nextstep — USEP price prediction
• nlp4ml — Python NLP wrapper
• nullpom — Library to easily run Null Importances.
• nyaggle — Code for Kaggle and Offline Competitions.
• octopus-ml — A collection of handy ML and data validation tools
• onnx-extended — Extends the list of supported operators in onnx reference implementation and onnxruntime, or implements faster versions in C++.
• openbox — Efficient and generalized blackbox optimization (BBO) system
• openfe — OpenFE: automated feature generation with expert-level performance
• openstef — Open short term energy forecaster
• openstf — Open short term forcasting
• OptGBM — Optuna + LightGBM \= OptGBM
• optuna — A hyperparameter optimization framework
• optuna-integration — Integration libraries of Optuna.
• oracle-ads — Oracle Accelerated Data Science SDK
• oracle-automlx — Automated Machine Learning with Explainability
• ordinalgbt — A library to build Gradient boosted trees for ordinal labels
• oscar-test0629 — For testing.
• pandaslearn — `pandaslearn` is a small wrapper on top of `scikit-learn` to automate common modeling tasks.
• paragrid — Simple parallelized grid search to find the best hyperparameters
• PatryksAutoAI — Auto_AI_patryk
• pegasuspy — Pegasus is a Python package for analyzing sc/snRNA-seq data of millions of cells
• penguin-libraries — Easy and useful libraries.
• perlib — Deep learning, Machine learning and Statistical learning for humans.
• permutation-feature-selector — A package for calculating permutation importance and selecting features.
• personalization — An end-to-end machine learning pipeline to train ml model and deploy it to realtime inference endpoint
• pgml-extension — Simple machine learning in PostgreSQL.
• phenopy — Phenotype comparison scoring by semantic similarity.
• PiML — A low-code interpretable machine learning toolbox in Python.
• PipelineTS — One-stop time series analysis tool, supporting time series data preprocessing, feature engineering, model training, model evaluation, model prediction, etc. Based on spinesTS and darts.
• pitci — Prediction intervals for trees using conformal intervals - pitci
• player-performance-ratings — Match Predictions based on Player Ratings
• pnow — A restful client library, designed to access predictnow restful API.
• pou-shap — A unified approach to explain the output of any machine learning model.
• pre-ai-python — Microsoft AI Python Package
• pre-reco-utils — Recommender System Utilities