general_class_balancer
Data matching algorithm for categorical and continuous variables (by mleming)
imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning (by scikit-learn-contrib)
| general_class_balancer | imbalanced-learn | |
|---|---|---|
| 1 | 1 | |
| 3 | 6,714 | |
| - | 0.7% | |
| 10.0 | 7.5 | |
| about 2 years ago | about 2 months 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.
general_class_balancer
Posts with mentions or reviews of general_class_balancer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-26.
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What’s your approach to highly imbalanced data sets?
Multivariate data matching. I wrote a function to do this in grad school: https://github.com/mleming/general_class_balancer
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!
What are some alternatives?
When comparing general_class_balancer and imbalanced-learn you can also consider the following projects:
deodel - A mixed attributes predictive algorithm implemented in Python.
ydata-synthetic - Synthetic data generators for tabular and time-series data
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
scikit-learn - scikit-learn: machine learning in Python