Reverse Dependencies of yellowbrick

The following projects have a declared dependency on yellowbrick:

• AI-Starter — Python library which is extensively used for all AI projects
• amlr — amlr - Auto Machine Learning Report
• autogluon.eda — AutoML for Image, Text, and Tabular Data
• automlkiller — Auto machine learning, deep learning library in Python.
• autotonne — Auto machine learning, deep learning library in Python.
• BaseML — BaseML provides numerous machine learning methods to quickly train and apply algorithms.
• chickenstats — A Python package for scraping & analyzing sports statistics
• civetqc — CivetQC is a command-line tool for automated quality control of CIVET outputs
• cytopy — Data centric algorithm agnostic cytometry analysis framework
• dcctk — Diachronic Character-based Corpus toolkit
• document-tracking — Algorithms to track documents and build news stories from them. It implements the Miranda et al. (2018) algorithm, as well as other alternatives and baselines to track documents.
• DXC-AI-MBN — Python library which is extensively used for all AI projects
• DXC-AI-Test — A Python package for DXC AI work
• DXC-AI-Test-3 — A Python package to add two numbers.
• DXC-Industrialized-AI-Starter — Python library which is extensively used for all AI projects
• DXC-RL — Python library which is extensively used for all AI projects
• ecmanalysis — Tools for analysis of the extracellular matrix
• fancylit — Contains pre-packaged Streamlit code to render fancy visualizations, run modeling tasks, and data exploration
• fiser-tools — Personal DS tools
• forecasttime — Python package to integrate the workflow of a variety of time series forecast methods
• GReNaDIne — Data-Driven Gene Regulatory Network Inference
• hgct — Hanzi Glyph Corpus Toolkit
• Hive-ML — Python package to run Machine Learning Experiments, within the Hive Framework.
• lolpop — A software engineering framework for machine learning workflows
• mlframe — mlframe package.
• MOBiceps — Python tools for Mass Spectrometry and Omics data.
• my-mltools — My machine learning toolkit.
• neptune-sklearn — Neptune.ai scikit-learn integration library
• offline-rce-results-module — no summary
• openpy-fxts — Various functions and classes for time series forecasting with Machine and Deep Learning
• pandaslearn — `pandaslearn` is a small wrapper on top of `scikit-learn` to automate common modeling tasks.
• predictionconsoandrea — Utilities package
• prenigma-automl — prenigma_automl - An open source, low-code machine learning library.
• prenigmaautoml — prenigma_automl - An open source, low-code machine learning library.
• punditkit — PunditKit: A GUI for Scikit-Learn Models
• 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.
• pyoptimus — Optimus is the missing framework for cleaning and pre-processing data in a distributed fashion.
• scalarpy — Welcome to ScalarPy!
• Simba-UW-no-tf — Toolkit for computer classification of complex social behaviors in experimental animals
• Simba-UW-tf — Toolkit for computer classification of complex social behaviors in experimental animals
• Simba-UW-tf-dev — Toolkit for computer classification of complex social behaviors in experimental animals
• stglance — stglance is a small collection of streamlit widgets (for machine learning) that you can include in your streamlit app.
• streamlit-yellowbrick — Streamlit component for Yellowbrick visualization library
• that-ml-library — A useful package for EDA and quick ML model building
• TrendFlow — A tool for literature research and analysis
• tuneta — Optimize financial technical indicators for machine learning
• wxyz-notebooks — notebook demos for experimental Jupyter widgets