Reverse Dependencies of lightgbm

The following projects have a declared dependency on lightgbm:

• predictnow-api — A restful client library, designed to access predictnow restful API.
• predictnow-client — A restful client library, designed to access predictnow restful api.
• prenigma-automl — prenigma_automl - An open source, low-code machine learning library.
• prenigmaautoml — prenigma_automl - An open source, low-code machine learning library.
• preprocess1 — no summary
• prettymetrics — One place metrics for various ML regression and classification algorithms
• prevision-quantum-nn — Prevision Automating Quantum Neural Networks Applications
• probatus — Validation of regression & classifiers and data used to develop them
• prolothar-rule-mining — algorithms for prediction and rule mining on event sequences
• pspso — pspso is a python package for selecting machine learning algorithms parameters.
• pureml — no summary
• pureml-evaluate — no summary
• py-automl-lib — Python package for automated hyperparameter-optimization of common machine-learning algorithms
• pybirds — Business Intelligence Risk Data Science
• pycaML — Python Comparative Analysis for Machine Learning
• pycaret — PyCaret - An open source, low-code machine learning library in Python.
• pycaret-nightly — Nightly version of PyCaret - An open source, low-code machine learning library in Python.
• pycaret-ts-alpha — PyCaret - An open source, low-code machine learning library in Python.
• pydimple — Proof-of-concept implementation of dimple (debiased inference made simple)
• pyDSlib — General utilities to streamline data science and machine learning routines in python
• pyemits — python package for easy manipulation on time series data for quick insight
• pyepal — PyePAL implemented the epsilon-PAL active learning algorithm
• pymatchingtools — A toolbox of common matching methods
• pymlpipe — PyMLpipe is a Python library for ease Machine Learning Model monitering and Deployment.
• pymlup — MLup framework, fast ml to production, easy to learn, easy to use.
• pymlx — Yet another machine learning framework
• pyoats — Quick and Easy Time Series Outlier Detection
• pyoe — Investigating open environment challenges in real-world relational data streams with PyOE.
• pyqlib — A Quantitative-research Platform
• pyRealEstate — package to assist with data analytics in real estate
• pyrecdp — A data processing bundle for spark based recommender system operations
• pyserini-install — A Python toolkit for reproducible information retrieval research with sparse and dense representations
• PySRAG — This Python package provides tools for analyzing and processing data related to Severe Acute Respiratory Syndrome (SARS) and other respiratory viruses. It includes functions for data preprocessing, feature engineering, and training Gradient Boosting Models (GBMs) for binary or multiclass classification.
• pytabkit — ML models + benchmark for tabular data classification and regression
• pythie-serving — A GRPC server to serve model types using tensorflow-serving .proto services
• python-allib — A typed Active Learning Library
• python-iArt — iArt: A Generalized Framework for Imputation-Assisted Randomization Tests
• python-sumo — **sumo** is a command-line tool to identify molecular subtypes in multi-omics datasets. It implements a novel nonnegative matrix factorization (NMF) algorithm to identify groups of samples that share molecular signatures, and provides additional modules to evaluate such assignments and identify features that drive the classification.
• python-vivid — Support Tools for Machine Learning VIVIDLY
• pythonqlib — A Quantitative-research Platform
• pytorch-frame — Tabular Deep Learning Library for PyTorch
• qshap — Exact computation of Shapley R-squared in polynomial time
• quantile-tree — Monotone quantile regressor
• quartic-sdk — QuarticSDK is the SDK package which exposes the APIs to the user
• quartic-sdk-gsk — QuarticSDK is the SDK package which exposes the APIs to the user
• quickerml — Machine learning toolkit to find the best starting model for your project
• rai-test-utils — Common basic test utilities used across various RAI tools
• raitracker — Responsible AI Toolbox Tracker
• raiwidgets — Interactive visualizations to assess fairness, explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
• ranktreeEnsemble — Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction.
• rapidgbm — RapidGBM is a powerful Python package designed to streamline the process of tuning LightGBM models using the optimization framework Optuna.
• rapidoml — RapidoML is a simple Automated Machine Learning (AutoML) library
• rapidpredict — rapid predict is a python package to simplifies the process of fitting and evaluating multiple machine learning models on a dataset.
• rdagent — Research & Development Agent
• RealEstate-package — МЛ-модель, предсказывающая стоимость недвижимости по её параметрам.
• recommendation-model-server — A real-time inference server
• recommenders — Recommenders - Python utilities for building recommendation systems
• rektgbm — No-brainer machine learning solution to achieve satisfactory performance
• responsibleai — SDK API to explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
• retrofit — AutoML, Forecasting, NLP, Image Classification, Feature Engineering, Model Evaluation, Model Interpretation, Fast Processing.
• rltrade-test — Easy to use Reinforcement Library for finance
• rumboost — Gradient Boosting Decision Trees for Random Utility Models
• run-models — Run all regression and classification models with its default parameters
• s2aff — Semantic Scholar's Affiliation Extraction: Link Your Raw Affiliations to ROR IDs
• s2cloudless — Sentinel Hub's cloud detector for Sentinel-2 imagery
• salesforce-merlion — Merlion: A Machine Learning Framework for Time Series Intelligence
• sapientml-core — A SapientML plugin of SapientMLGenerator
• scalarpy — Welcome to ScalarPy!
• scCloud — scRNA-Seq analysis tools that scale to millions of cells
• scEvoNet — Tool for generation [cell state - gene program] network
• scikit-digital-health — Python general purpose human motion inertial data processing package.
• scikit-physlearn — A machine learning library for regression.
• sdqcpy — SDQCPy is a comprehensive Python package designed for synthetic data management, quality control, and validation.
• seaborn-analyzer — seaborn-analyzer: data visualization of regression, classification and distribution
• Seance — A Wrapper around MLForecast.
• secml-malware — no summary
• selective — feature selection library
• sensai — The Python library for sensible AI
• Sentinel-imgpackage — Sentinel satellite image module
• sf-hamilton — Hamilton, the micro-framework for creating dataframes.
• sfdc-merlion — Merlion: A Machine Learning Framework for Time Series Intelligence
• shap — A unified approach to explain the output of any machine learning model.
• shap-app — A comprehensive application for interpreting machine learning models using SHAP values
• shapash — Shapash is a Python library which aims to make machine learning interpretable and understandable by everyone.
• shaperone — Shaperone is a fork of the SHAP library, fixing open issues to improve usability.
• shortcutml — Machine learning baseline prototyping tools
• SIAC — A sensor invariant Atmospheric Correction (SIAC)
• sibyl-ai — Wrapper for SKLearn Pipeline with Auto ML features
• simager — Simple tools for auto classification and text preprocessing
• simpml — SimpML is an open-source, no/low-code machine learning library in Python that simplifies and automates machine learning workflows.
• singletrader — a package for backtesting and factor analysis
• skforecast — Skforecast is a Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
• sklearn-utilities — Utilities for scikit-learn.
• sklearndf — Data frame support and feature traceability for `scikit-learn`.
• skops — A set of tools to push scikit-learn based models to and pull from Hugging Face Hub
• skt — SKT package
• sktmls — MLS SDK
• slim-trees — A python package for efficient pickling of ML models.
• smart-data-science — Personal side project to streamline the most common tasks of data science solutions in an efficient manner. This project is based on my experience as a lead data scientist in the industry and financial services sectors, where I have gained expertise in delivering effective data-driven insights and solutions
• smartpredict — An advanced machine learning library for effortless model training, evaluation, and selection.