ShimantoRahman/empulse
Value-driven and cost-sensitive analysis for scikit-learn
This helps data professionals, business analysts, or risk managers build and evaluate machine learning models that account for the real-world financial costs of making mistakes. You feed it your data and define the monetary costs associated with correct or incorrect predictions (like lost revenue from a missed customer or the expense of a false alarm). It then outputs models optimized to minimize those specific costs, rather than just raw accuracy.
Used by 1 other package. Available on PyPI.
Use this if you need your predictive models to be evaluated and optimized based on the actual financial impact of their decisions, rather than just how often they are right or wrong.
Not ideal if your primary goal is basic model accuracy without needing to factor in specific, quantifiable monetary costs for different types of errors.
Stars
32
Forks
3
Language
Python
License
—
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
Dependencies
8
Reverse dependents
1
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