gxf1027/randomforests
C++ implementation of random forests classification, regression, proximity, variable importance and imputation.
This tool helps data scientists and analysts make predictions or categorize data by building a 'forest' of decision trees. You input structured numerical datasets, and it outputs a trained model that can classify new data, predict values, or identify important features. It's designed for practitioners who need robust, interpretable models for complex data problems.
Use this if you need to build predictive models for classification (e.g., categorizing emails as spam) or regression (e.g., predicting house prices) from numerical data, and want options for feature importance, outlier detection, and handling missing values.
Not ideal if your primary goal is real-time, ultra-low-latency predictions on simple models, or if you are working with unstructured data like images or text without prior feature extraction.
Stars
21
Forks
4
Language
C++
License
GPL-2.0
Category
Last pushed
Jan 10, 2026
Commits (30d)
0
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