Reverse Dependencies of category-encoders

The following projects have a declared dependency on category-encoders:

• adcl — Data preprocessing and cleaning tools for data science projects
• alphapy — AlphaPy: A Machine Learning Pipeline for Speculators
• altena — Feature extraction for categorical variables
• anai-opensource — Automated ML
• anonypyx — Anonymization library for python, fork of anonypy
• atom-ml — A Python package for fast exploration of machine learning pipelines
• auto-preprocess — Auto PreProcess package
• AutoFeatSelect — Automated Feature Selection & Feature Importance Calculation Framework
• automl-alex — State-of-the art Automated Machine Learning python library for Tabular Data
• automl-tools — automl_tools
• automlkiller — Auto machine learning, deep learning library in Python.
• autotonne — Auto machine learning, deep learning library in Python.
• ballet — Core functionality for lightweight, collaborative data science projects
• bamboos — Wrapper Functions for pandas, numpy, and scikit learn
• baseline-optimal — no summary
• batch-prediction-pipeline — no summary
• batch-prediction-pipeline-self — no summary
• bayte — Bayesian target encoding with scikit-learn and scipy
• bciavm — bciAVM is a machine learning pipeline used to predict property prices.
• blackfox-extras — BlackFox Extras
• bluecast — A lightweight and fast automl framework
• categorical-encoding — categorical encoding for featuretools
• CategoryReplacer — Categorical Features replace to Numerical Values.
• CEval — A package for evaluating the performance of counterfactual explainers.
• climaticai — climaticai is a library that builds, optimizes, and evaluates machine learning pipelines
• crosspredict — package for easy crossvalidation
• data-drift-detector — Compare differences between 2 datasets to identify data drift
• datarobotx — DataRobotX is a collection of DataRobot extensions
• datawaza — Datawaza is a collection of tools for data exploration, visualization, data cleaning, pipeline creation, model iteration, and evaluation.
• dbestclient — Model-based Approximate Query Processing (AQP) engine.
• deepchecks — Package for validating your machine learning model and data
• deeptables — Deep-learning Toolkit for Tabular datasets
• DMWT — no summary
• dnattend — AutoML classifier for predicting patient non-attendance (DNA)
• dynapipe — Dynamic Pipeline is a high-level API to help data scientists building models in ensemble way, and automating Machine Learning workflow with simple coding.
• e2eml — An end-to-end solution for automl
• ehrapy — Electronic Health Record Analysis with Python.
• EtaML — An automated machine learning platform with a focus on explainability
• 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
• fastai-category-encoders — Category encoders integrated with Fast.ai
• featurewiz — Select Best Features from your data set - any size - now with XGBoost!
• foreshadow — Peer into the future of a data science project
• future-sales — no summary
• fuzzylearn — no summary
• g-batch-prediction-pipeline — no summary
• GAForecast — Prediction library for tabular data powered by neural network.
• gama — A package for automated machine learning based on scikit-learn.
• gargaml — A personal ML lib
• gentab — A synthetic tabular data generation library.
• grading-tools — Tools for evaluating student submissions.
• GradTree — A novel method for learning hard, axis-aligned decision trees with gradient descent.
• GRANDE — A novel ensemble method for hard, axis-aligned decision trees learned end-to-end with gradient descent.
• gretel-synthetics — Synthetic Data Generation with optional Differential Privacy
• hivecode — Hivecode is a versatile and comprehensive Python library, with a focus on efficiency and reusability, Hivecode empowers developers and data enthusiasts alike to streamline their projects.
• house-prices — no summary
• HyPSTER — HyPSTER is a brand new Python package that helps you find compact and accurate ML Pipelines while staying light and efficient
• iguanas — Rule generation, optimisation, filtering and scoring library
• koleksyon — A collection of statistical functions and data manipulation functions and methods for use in data science projects
• kolibri-ml — Deep Learning and more NLP toolkit
• kolmogorov-abacus — A/B experiments planning and evaluation tool
• kts — A framework for fast and interactive conducting machine learning experiments on tabular data
• lale — Library for Semi-Automated Data Science
• lazytransform — Clean your data using a scikit-learn transformer in a single line of code
• lcd-classification-model — Example classification model package by Neidu.
• lp-Aicloud — this a aicloud
• luntaiDs — Make Data Scientist life Easier Tool
• meta-learn — Collection and utilization of metadata from machine learning models and problems.
• 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.
• mlmachine — Accelerate machine learning experimentation
• mlops_batch_prediction_pipeline — no summary
• mosaic-utils — Utils library for Mosaic
• mrmr-selection — minimum-Redundancy-Maximum-Relevance algorithm for feature selection
• nodegam — NodeGAM - an interpretable deep learning GAM model.
• nyaggle — Code for Kaggle and Offline Competitions.
• OnePiecePredictor — Hyper Paramter Tuning and Models performance comparison
• openimis-be-claim-ai — The openIMIS Backend Claim AI reference module.
• openml-speed-dating-pipeline-steps — This contains all the steps needed to reproduce the OpenML pipeline steps
• opoca — Opoca library aims to drastically speed up producing proof of concepts (PoC) for machine learning projects.
• optimalflow — OptimalFlow is an Omni-ensemble Automated Machine Learning toolkit to help data scientists building optimal models in easy way, and automate Machine Learning workflow with simple code.
• oracle-automlx — Automated Machine Learning with Explainability
• oracle-guardian-ai — Oracle Guardian AI Open Source Project
• oreum-core — Core tools for use on projects by Oreum Industries
• pada — a ligthweight feature manage framework
• pandaslearn — `pandaslearn` is a small wrapper on top of `scikit-learn` to automate common modeling tasks.
• pardon — The Data Transformation and Machine Learning Accelerator
• paso — A python package for the entire data machine learning pipeline
• pdtr — 自动决策树规则挖掘工具包
• pec-model-master — no summary
• pertCF — PertCF explainer is a counterfactual based XAI technique.
• photobox — A guizero application to image insect sticky plates
• pnow — A restful client library, designed to access predictnow restful API.
• pre-reco-utils — Recommender System Utilities
• predictnow — A restful client library, designed to access predictnow restful API.
• predictnow-api — A restful client library, designed to access predictnow restful API.
• prep-ml — Preprocessing for ML models made easy.
• psod — Outlier detection using supervised methods in an unsupervised context
• psusannx-pipeline — A package containing classes to be used in the preprocessing data pipeline for the PSUSANNX project.
• pycaret — 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.