Reverse Dependencies of shap
The following projects have a declared dependency on shap:
- ferret-xai — A python package for benchmarking interpretability approaches.
- fife — Finite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and multivariate time series forecasting
- fifids — data science for swiss cheese brains
- fightchurn — Code from the book Fighting Churn With Data
- FIRSTBEATLU — This package contains several methods for calculating Conditional Average Treatment Effects
- fklearn — Functional machine learning
- flexcv — Easy and flexible nested cross validation for tabular data in python.
- flood-forecast — An open source framework for deep time series forecasting and classfication built with PyTorch.
- flowcept — FlowCept is a runtime data integration system that empowers any data processing system to capture and query workflow provenance data using data observability, requiring minimal or no changes in the target system code. It seamlessly integrates data from multiple workflows, enabling users to comprehend complex, heterogeneous, and large-scale data from various sources in federated environments.
- forecasttime — Python package to integrate the workflow of a variety of time series forecast methods
- gamma-facet — Human-explainable AI.
- gdp-time-series — no summary
- giotto-time — Toolbox for Time Series analysis and integration with Machine Learning.
- giskard — The testing framework dedicated to ML models, from tabular to LLMs
- gitlabds — Gitlab Data Science and Modeling Tools
- greenflow-hrp-plugin — no summary
- GuangBEAT — BEAT
- GuangTestBeat — This package contains several methods for calculating Conditional Average Treatment Effects
- h1st — Human-First AI (H1st)
- h1st-contrib — Human-First AI (H1st)
- hbac-bias-detection — no summary
- hcga — Highly comparative graph analysis
- hiclass — Hierarchical Classification Library.
- hipe4ml — Minimal heavy ion physics environment for Machine Learning
- hisoka — no summary
- holisticai — Holistic AI Library
- hypergbm — A full pipeline AutoML tool integrated various GBM models
- ibm-metrics-plugin — IBM Watson OpenScale Metrics library
- Icube-radiomics — Perform Radiomics Feature Extraction and Machine Learning
- idsw — Full workflow for ETL, statistics, and Machine learning modelling of (usually) time-stamped industrial facilities data.
- impactchart — A package for generating impact charts.
- insolver — Insolver is low-code machine learning library, initially created for the insurance industry.
- intel-xai — Intel® Explainable AI Tools
- IntelliGenes — IntelliGenes: AI/ML pipeline for predictive analyses using multi-genomic profiles.
- intelligenzaartificiale — Intelligenza Artificiale la libreria python italiana dedicata all'I.A.
- interpret-community — Microsoft Interpret Extensions SDK for Python
- interpret-core — Fit interpretable machine learning models. Explain blackbox machine learning.
- 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
- iohxplainer — eXplainable Benchmarking for Iterative Optimization Heuristics
- IREX — no summary
- isv — Automated Interpretation of Structural Copy Number Variants
- itershap — Iterative feature selection method using SHAP values
- itlubber-automl — https://zhuanlan.zhihu.com/p/447307569
- iWork — description
- jori-autoprognosis — Test
- kabbes-ml-pipeline — A centralized pattern for creating Machine Learning pipelines
- kf-d3m-primitives — All Kung Fu D3M primitives as a single library
- LASExplanation — This package is a brief wrap-up toolkit built based on 2 explanation packages: LIME and SHAP. The package contains 2 explainers: LIMEBAG and SHAP. It takes data and fitted models as input and returns explanations about feature importance ranks and/or weights. (etc. what attributes matter most within the prediction model).
- lazyqsar — A library to quickly build QSAR models
- leapfrog — Boost productivity by using a range of tools helping with ETL, modelling, reporting, and dashboards
- lightgbmlss — LightGBMLSS - An extension of LightGBM to probabilistic modelling.
- lightwood — Lightwood is Legos for Machine Learning.
- LingerGRN — Gene regulatory network inference
- LISA-CNN-ExplainerV1 — Unified Explanation Provider For CNNs
- LISA-CNN-ExplainerV2 — Unified Explanation Provider For CNNs
- LISA-CNN-ExplainerV3 — Unified Explanation Provider For CNNs
- LISA-CNN-ExplainerV4 — Unified Explanation Provider For CNNs
- LISA-CNN-ExplainerV5 — Unified Explanation Provider For CNNs
- litelearn — a python library for quickly building and evaluating models
- 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.
- lp-Aicloud — this a aicloud
- 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
- lux-explainer — Universal Local Rule-based Explainer
- LZBEAT — This package contains several methods for calculating Conditional Average Treatment Effects
- machnamh — An ipywidgets based package for detecting bias in ML data and Models
- machnamh-unmakingyou — An ipywidgets based package for detecting bias in ML data and Models
- mango — Library with a collection of usefull classes and methods to DRY
- MassiveQC — Tools for QC massive RNA-seq samples
- mastml — MAterials Simulation Toolkit - Machine Learning
- mercs-mixed — MERCS: Multi-Directional Ensembles of Regression and Classification treeS
- metats — Meta-Learning for Time Series Forecasting
- microbiome-toolbox — Microbiome Toolbox
- minmlst — Machine-learning based minimal MLST scheme for bacterial strain typing
- ML-medic-kit — The Machine Learning Medic Kit is designed to enhance the capabilities of health data scientists tackling binary classification problems
- mlcomposer — Treinamento e avaliação de modelos de machine learning através de funções e classes encapsuladas
- mlearner — Machine Learning Library Extensions
- mlflowgo — no summary
- mlmachine — Accelerate machine learning experimentation
- mlops-build — MLops course
- mlrap — Machine Learning Regression Analyse Packages
- MOBiceps — Python tools for Mass Spectrometry and Omics data.
- model-monitoring — Model Monitoring
- modelcomp — Compare between performances of machine learning models with ease.
- modelflow — Machine Learning flowing from start to finish
- moleculex — MoleculeX: a new and rapidly growing suite of machine learning methods and software tools for molecule exploration
- mvbep — Measurement and Verification Building Energy Prediction (MVBEP) is an open-source framework for developing data-driven models for predicting the building baseline energy consumption and estimating savings associated with retrofitting in the post-retrofit period.
- my-recommending — no summary
- myautoml — myautoml is a package which provides a framework to automate machine learning
- neurocat — Interface Design for Neurocat's Research Engineer Test
- omnixai — OmniXAI: An Explainable AI Toolbox
- openbox — Efficient and generalized blackbox optimization (BBO) system
- openpredict — A package to help serve predictions of biomedical concepts associations as Translator Reasoner API.
- optuna-integration — Integration libraries of Optuna.
- oracle-automlx — Automated Machine Learning with Explainability
- Orange3-Explain — Orange3 add-on for explanatory AI