deodel
imbalanced-learn
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| deodel | imbalanced-learn | |
|---|---|---|
| 13 | 1 | |
| 5 | 6,697 | |
| - | 0.8% | |
| 6.3 | 7.4 | |
| 2 months ago | 28 days ago | |
| Python | Python | |
| - | MIT License |
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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?
imbalanced-learn
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What’s your approach to highly imbalanced data sets?
There's a pletora of undersampling and oversampling models you can try out. To avoid removing information form the dataset, you can focus on oversampling techniques. You can try imbalanced-learn or smote-variants. Given enough data, using fully synthetic data is also an option, you can check ydata-synthetic for it. Let us know how it turned out!
What are some alternatives?
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
ydata-synthetic - Synthetic data generators for tabular and time-series data
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
general_class_balancer - Data matching algorithm for categorical and continuous variables
grape - 🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
scikit-learn - scikit-learn: machine learning in Python
misc
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.