BotLibre
deodel
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| BotLibre | deodel | |
|---|---|---|
| 1 | 13 | |
| 563 | 5 | |
| 0.5% | - | |
| 6.6 | 6.3 | |
| 27 days ago | 2 months ago | |
| Java | Python | |
| Eclipse Public License 1.0 | - |
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BotLibre
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?
learn - Neuro-symbolic interpretation learning (mostly just language-learning, for now)
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
grape - 🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
simplenlg - Java API for Natural Language Generation. Originally developed by Ehud Reiter at the University of Aberdeen’s Department of Computing Science and co-founder of Arria NLG. This git repo is the official SimpleNLG version.
ydata-synthetic - Synthetic data generators for tabular and time-series data
DKPro Core - Collection of software components for natural language processing (NLP) based on the Apache UIMA framework.
misc
sematle - NLU service that converts plain English to known and structured data.
general_class_balancer - Data matching algorithm for categorical and continuous variables
Apache OpenNLP - Apache OpenNLP
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.