imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning (by scikit-learn-contrib)
deodel
A mixed attributes predictive algorithm implemented in Python. (by c4pub)
| imbalanced-learn | deodel | |
|---|---|---|
| 1 | 13 | |
| 7,070 | 5 | |
| 0.3% | - | |
| 6.9 | 3.0 | |
| 4 months ago | about 1 year ago | |
| Python | Python | |
| 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.
imbalanced-learn
Posts with mentions or reviews of imbalanced-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-26.
- 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!
deodel
Posts with mentions or reviews of deodel. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-17.
- [P] New predictor does classification intermixed with regression
- Easy Machine Learning Dataset Evaluation Tool (Update)
- 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:
- 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)
- [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?
When comparing imbalanced-learn and deodel you can also consider the following projects:
general_class_balancer - Data matching algorithm for categorical and continuous variables
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
confidenceinterval - The long missing library for python confidence intervals
grape - 🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
zilean - Python package that facilitates machine learning tasks on League of Legends matches.
dcai-lab - Lab assignments for Introduction to Data-Centric AI, MIT IAP 2024 👩🏽💻