JohnNay/datafsm

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

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Emerging

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.

No commits in the last 6 months.

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.

decision-modeling behavioral-analysis social-science-research economic-modeling time-series-prediction
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 16 / 25

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Stars

17

Forks

6

Language

R

License

MIT

Last pushed

Jun 04, 2021

Commits (30d)

0

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