aclai-lab/ModalDecisionTrees.jl
Julia implementation of Modal Decision Trees & Forests, for interpretable classification of spatial and temporal data. Long live Symbolic Learning!!
This tool helps data analysts and scientists classify complex time-series or image data to identify patterns and make predictions. You feed in your raw temporal data (like audio or sensor readings) or spatial data (like aerial images) and get back an interpretable decision model. It's ideal for practitioners who need to understand why a classification was made, rather than just getting a prediction.
Use this if you need to classify time series or image data and require an interpretable model that clearly shows the temporal or spatial rules driving its decisions.
Not ideal if your data contains text, missing values, or non-numeric features, or if you don't need to understand the underlying decision logic.
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Julia
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Last pushed
Jan 30, 2026
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