Reverse Dependencies of lightgbm

The following projects have a declared dependency on lightgbm:

• cat-spend-training-pipeline — no summary
• causalml — Python Package for Uplift Modeling and Causal Inference with Machine Learning Algorithms
• causalnlp — CausalNLP: A Practical Toolkit for Causal Inference with Text
• 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.
• classifier-toolkit — no summary
• clearbox-sure — no summary
• cleen — Unveiling Insights Through Automated Feature Range Analysis.
• climaticai — climaticai is a library that builds, optimizes, and evaluates machine learning pipelines
• climl — no summary
• cloudexplain — A package for explaining cloud-based solutions provided by cloudexplain GmbH.
• cmip-metric — no summary
• cnapps — Predict CN cross-coupling reaction yield
• codcat — Code snippets language classification tool
• compactem — compactem
• concrete-ml-extensions-hb — Convert trained traditional machine learning models into tensor computations
• core-of-theaisphere — Package sitting at the core of theAIsphere
• cpadapter — This package adapts different models in order to create confidence intervals using conformal prediction
• crosspredict — package for easy crossvalidation
• crossval-ensemble — A scikit-learn wrapper for CrossValidation Ensembles
• curve-linear — Linear model of monotonic curves of each feature
• cv-pruner — Three-layer Pruning for Nested Cross-Validation to Accelerate Automated Hyperparameter Optimization for Embedded Feature Selection in High-Dimensional Data With Very Small Sample Sizes
• cyfi — Estimate cyanobacteria density in small, inland water bodies using Sentinel-2 satellite imagery.
• czsc — 缠中说禅技术分析工具
• d3m-common-primitives — D3M common primitives
• darwin-mendel — Genetic Algorithm: Optimize the output of machine learning models
• dask-lightgbm — LightGBM distributed training on Dask
• data-dashboard — Dashboard to explore the data and to create baseline Machine Learning model.
• data-iq — Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
• data-science-pipeline-automation — Python library to help you to automate the data science pipeline
• datarobotx — DataRobotX is a collection of DataRobot extensions
• datascienv — Data Science package for setup data science environment in single line
• datrics-json — Open source library for the Datrics models deserialization
• dccc-scoring-model — Example machine learning package from Curtis Lu. Modified from the udemy course: deploying machine learning models
• dddex — The package 'data-driven density estimation x' (dddex) turns any standard point forecasting model into an estimator of the underlying conditional density
• deepBreaks — deepBreaks: a machine learning tool for identifying and prioritizing genotype-phenotype associations
• deeptables — Deep-learning Toolkit for Tabular datasets
• detectree — Tree detection from aerial imagery in Python.
• df-analyze — Add your description here
• didtool — Tool set for feature engineering & modeling
• digen — DIGEN: Diverse Generative ML Benchmark
• dj-kaggle-pipeline — Pipelines and utility classes for Kaggle and data science!
• dm-utils — Data Mining Utils
• domino-code-assist — no summary
• doona-taos-edge-model — TAOS Edge Model 예측 패키지
• ds-boost — Package for Practical & efficient Data Science in Python. Initially written for data-science-keras repo
• ds-core-sanpier — A package to automize some of the steps before modeling and in the modeling stage
• dsbundle — Streamline your data science setup with dsbundle in one effortless install.
• dtreeviz — A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization
• DumME — Mixed Effects Dummy Model
• dvclive — Experiments logger for ML projects.
• e2eml — An end-to-end solution for automl
• eazeml — EazeML makes Task of Machine Learning and Data Science super easy.
• ebes — EBES: Easy Benchmarking for Event Sequences.
• ebonite — Machine Learning Lifecycle Framework
• econml — This package contains several methods for calculating Conditional Average Treatment Effects
• edelweiss — emulator for various applications
• eland — Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
• electriceel — Model electriceel for model feature metric
• energy-consumption-forecasting — A Machine Learning project on Denmark's Energy Consumption.
• entmoot — no summary
• environmental-insights — A Python package for democratizing access to ambient air pollution data and predictive analytics.
• eo-grow — Earth observation framework for scaled-up processing in Python
• equal-odds — _PACKAGE UNDER CONSTRUCTION_
• erroranalysis — Core error analysis APIs
• eschergraph — The library that uses AI agents to enable building and searching in generalized knowledge graphs.
• evalml — an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions
• evolutionary-forest — An open source python library for automated feature engineering based on Genetic Programming
• EXGEP — A framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models
• explain-spike — Package that contains several methods and functions for explaining and understanding machine learning models
• extralearning — no summary
• FAMEwork — Framework for Adversarial Malware Evaluation
• fast-machine-learning — fast-machine-learning
• fasttreeshap — A fast implementation of TreeSHAP algorithm.
• fathom-lib — Fathom lib
• feat-engine — no summary
• featimp — Feature importance for machine learning
• featq — Feature Engineering with Q-learning
• feature-selector — FeatureSelector is a class for removing features for a dataset intended for machine learning
• feature-utils — Utilities for generating features for machine learning pipelines
• featuresfinder — featuresfinder is a python package for feature extration using nearly 6 different algorithsm.
• featurewiz — Select Best Features from your data set - any size - now with XGBoost!
• fedot — Automated machine learning framework for composite pipelines
• fife — Finite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and multivariate time series forecasting
• FIRSTBEATLU — This package contains several methods for calculating Conditional Average Treatment Effects
• fklearn — Functional machine learning
• FLAML — A fast library for automated machine learning and tuning
• flashML — AutoML tool
• FlexAutoML — A machine learning toolbox
• FlexML — Easy-to-use and flexible AutoML library for Python
• flimsay — A super simple fast IMS predictor
• fold-core — A Time Series Cross-Validation library that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour.
• forecastflowml — Scalable machine learning forecasting framework with Pyspark
• forecastga — A Python tool to forecast GA data using several popular timeseries models