LinearBoost/linearboost-classifier
LinearBoost Classifier is a rapid and accurate classification algorithm that builds upon a very fast, linear classifier.
This tool helps data scientists and machine learning engineers quickly and accurately categorize new data based on historical patterns. You provide structured data with known labels, and it produces a model that can predict labels for new, unseen data, outperforming other methods in speed and accuracy. It's designed for professionals building predictive systems.
224 stars.
Use this if you need to build a highly accurate classification model that trains and predicts much faster than traditional methods like XGBoost or LightGBM, especially with large datasets or when working with imbalanced data.
Not ideal if your primary goal is interpretability of individual feature contributions rather than raw predictive performance, or if you need to perform tasks other than classification.
Stars
224
Forks
22
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 07, 2026
Commits (30d)
0
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