yellowbrick VS pytea

Compare yellowbrick vs pytea and see what are their differences.

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yellowbrick pytea
2 3
4,201 310
0.4% 0.3%
2.8 1.8
9 months ago about 2 years ago
Python TypeScript
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

yellowbrick

Posts with mentions or reviews of yellowbrick. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

pytea

Posts with mentions or reviews of pytea. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

What are some alternatives?

When comparing yellowbrick and pytea you can also consider the following projects:

kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

Anaconda - Anaconda turns your Sublime Text 3 in a full featured Python development IDE including autocompletion, code linting, IDE features, autopep8 formating, McCabe complexity checker Vagrant and Docker support for Sublime Text 3 using Jedi, PyFlakes, pep8, MyPy, PyLint, pep257 and McCabe that will never freeze your Sublime Text 3

cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both

itermplot - An awesome iTerm2 backend for Matplotlib, so you can plot directly in your terminal.

uncertainty-toolbox - Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

seaborn-image - High-level API for attractive and descriptive image visualization in Python

WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

fpdf2 - Simple PDF generation for Python

vivit - [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps

scikit-survival - Survival analysis built on top of scikit-learn

Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.