tabmat
Efficient matrix representations for working with tabular data (by Quantco)
mixed-naive-bayes
Naive Bayes with support for categorical and continuous data (by remykarem)
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| tabmat | mixed-naive-bayes | |
|---|---|---|
| 1 | 1 | |
| 102 | 63 | |
| 2.0% | - | |
| 8.4 | 2.5 | |
| 6 days ago | about 1 year ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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.
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.
tabmat
Posts with mentions or reviews of tabmat.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-10-11.
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[P] glum: High performance Python generalized linear modeling, a glmnet alternative!
We're also releasing tabmat (https://github.com/Quantco/tabmat/), a tabular matrix backend for glum. It supports mixes of dense, sparse and categorical matrices. On some operations, tabmat is 50x faster than scipy.sparse! And it's memory-efficient.
mixed-naive-bayes
Posts with mentions or reviews of mixed-naive-bayes.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[Discussion] Unique uses of recommendation systems?
Some of the features are categorical, such as request type (is it about troubleshoot, price request, etc.), product, language, SLA, etc.; and some are continuous, namely an embedding vector generated from the ticket free form text. Then we use this library that allows the training of a Naive Bayes model using mixed type of features (categorical and continuous).
What are some alternatives?
When comparing tabmat and mixed-naive-bayes you can also consider the following projects:
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
twitter-stock-sentiment - Use twitter to get live and trending stock sentiment!
pycm - Multi-class confusion matrix library in Python
system-design-primer - Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
glum - High performance Python GLMs with all the features!
Sparsebit - A model compression and acceleration toolbox based on pytorch.