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Behind the app: How raw data becomes real-time bike predictions
#V4T0A11 Reference https://posit.co/blog/building-data-pipelines-in-python-r/ Created with curiosity, this content is part of my ongoing...
CHUN-YUAN CHEN
Sep 7
Behind the curtain: Hyperparameters vs. Parameters
#Z9T1F27 Reference https://towardsdatascience.com/parameters-and-hyperparameters-aa609601a9ac/ Created with curiosity, this content is...
CHUN-YUAN CHEN
Jun 26
Breaking open the ML black box: SHAP vs. LIME
#N1B3V58 References https://medium.com/cmotions/opening-the-black-box-of-machine-learning-models-shap-vs-lime-for-model-explanation-d7bf5...
CHUN-YUAN CHEN
Mar 11
From prediction to reality: The calibration check
#D8A4F77 References https://medium.com/@sahilbansal480/understanding-model-calibration-in-machine-learning-6701814dbb3a...
CHUN-YUAN CHEN
Mar 11
ML metrics: Decode performance
#T0L9C81 References Çorbacıoğlu, Ş. K., & Aksel, G. (2023). Receiver operating characteristic curve analysis in diagnostic accuracy...
CHUN-YUAN CHEN
Mar 10
Overfitting vs. Underfitting: Nail the right fit
#B8K7Z32 Reference https://medium.com/@chaitanyasawant/overfitting-and-underfitting-common-problems-in-machine-learning-e9c8451a8410...
CHUN-YUAN CHEN
Mar 8
Imbalanced data: Upsampling and Downsampling
#R5Y8C66 Reference https://medium.com/codex/handling-imbalanced-data-upsampling-and-downsampling-in-machine-learning-10f33ff0620b Created...
CHUN-YUAN CHEN
Mar 2
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