SDNist: Benchmark data and evaluation tools for data synthesizers.
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Updated
Jun 18, 2024 - HTML
SDNist: Benchmark data and evaluation tools for data synthesizers.
BD2K Workshop for exploring predictive variables in Cardiovascular Risk
UNECE HLG-MOS Synthetic Data Challenge (TEAM DESTATIS)
Documentation for Data Caterer
Generate synthetic data in the browser
⛷ This study compared multiple linear regression (MLR) and decision tree models to predict skiers' run counts. The MLR model explained 82% of variance, while both models showed an average prediction error of ~2 runs. Overall, MLR outperformed the decision tree in predicting run count variance.
A demonstration of the MST method using the 1% 2011 Census Teaching File
We are privately owned small organization. The main purpose is the development of graphical interfaces, synthetic data generation, tensorflow supported test suite and software that helps to improve data -> narrowAI -> human -> information.
Repository for Slide Deck and Code Examples for talk at SDP Convening 2023
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