mixed-naive-bayes
deodel
| mixed-naive-bayes | deodel | |
|---|---|---|
| 1 | 13 | |
| 63 | 5 | |
| - | - | |
| 2.5 | 6.3 | |
| about 1 year ago | 3 months ago | |
| Python | Python | |
| MIT License | - |
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mixed-naive-bayes
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[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).
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?
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misc
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