tabmat
deodel
Our great sponsors
| tabmat | deodel | |
|---|---|---|
| 1 | 13 | |
| 102 | 5 | |
| 2.0% | - | |
| 8.4 | 6.3 | |
| 6 days ago | 2 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | - |
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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
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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.
deodel
- [P] New predictor does classification intermixed with regression
- Easy Machine Learning Dataset Evaluation Tool (Update)
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What are some practical tips for efficiently handling missing or null values in datasets during data analysis in Python?
You could use this new classifier deodel that is very robust. It deals seamlessly with missing data, nulls, mixed numerical and categorical attributes, and multi-class targets. You can see an application with this tool:
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What’s your approach to highly imbalanced data sets?
Just to mention that there is also a new algorithm that is immune to the imbalance of data. An implementation in python is available at: - https://github.com/c4pub/deodel
- Robust mixed attributes classifier (machine learning)
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The deodel classifier can act as a quick dataset evaluation tool. If your data is available in table format, you can check its potential for prediction/classification. Just feed it to deodel. It accepts mixed attributes without any preliminary curation. It simply considers attribute values expressed as floats (dot decimal) as being continuous. It accepts even a mix of continuous and categorical values for the same attribute column.
- [D] Open-source package to mix numerical, categorical and text features?
- [P] Discretization: equal-width trumps equal-frequency?
- [P] Discretization: equal-width beats equal-frequency?
What are some alternatives?
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
pycm - Multi-class confusion matrix library in Python
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
glum - High performance Python GLMs with all the features!
grape - 🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
Sparsebit - A model compression and acceleration toolbox based on pytorch.
ydata-synthetic - Synthetic data generators for tabular and time-series data
mixed-naive-bayes - Naive Bayes with support for categorical and continuous data
misc
general_class_balancer - Data matching algorithm for categorical and continuous variables
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.