JohnNay/datafsm
Machine Learning Finite State Machine Models from Data with Genetic Algorithms
This tool helps researchers and analysts understand complex, dynamic decision-making processes. By providing your panel data with binary predictor variables and an outcome variable, it generates an interpretable finite-state machine model. This model predicts future decisions and reveals the underlying rules governing how decisions change over time, making it useful for social scientists, economists, or anyone studying sequential choices.
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Use this if you need to build predictive models of dynamic decision-making from time-series data with binary inputs and an outcome, and you want an interpretable model rather than a black box.
Not ideal if your data doesn't fit a panel data format, your predictor variables are not binary (without conversion), or if you need a real-time, ultra-low-latency prediction system.
Stars
17
Forks
6
Language
R
License
MIT
Category
Last pushed
Jun 04, 2021
Commits (30d)
0
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