Reverse Dependencies of lightgbm
The following projects have a declared dependency on lightgbm:
- climaticai — climaticai is a library that builds, optimizes, and evaluates machine learning pipelines
- climl — no summary
- 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.
- 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!
- domino-code-assist — no summary
- 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
- 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.
- ebonite — Machine Learning Lifecycle Framework
- econml — This package contains several methods for calculating Conditional Average Treatment Effects
- 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.
- eo-grow — Earth observation framework for scaled-up processing in Python
- equal-odds — _PACKAGE UNDER CONSTRUCTION_
- erroranalysis — Core error analysis APIs
- 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
- 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
- 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
- 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
- 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
- g-batch-prediction-pipeline — no summary
- g-training-pipeline — no summary
- 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.
- HEBO — Heteroscedastic evolutionary bayesian optimisation
- 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