sibirbil/OCDT
Output-Constrained Decision Trees (OCDT)
This project helps data scientists and machine learning engineers build more reliable predictive models for real-world applications where predictions must follow specific rules. It takes your dataset and a set of business or scientific constraints, then produces a decision tree or random forest model whose predictions always adhere to those rules. This ensures that the model's outputs are not only accurate but also practical and feasible.
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Use this if you need to build regression models where predictions (like sales forecasts, resource allocations, or scientific measurements) must satisfy specific business or physical constraints, such as 'total sales must equal overall market size' or 'energy consumption cannot exceed facility capacity'.
Not ideal if your prediction task does not involve multi-target regression or if your predictions do not need to adhere to any domain-specific rules.
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Python
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Last pushed
Jun 09, 2025
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