Three principles of data science: predictability, computability, and stability (PCS)
By Bin Yu. 2018
38 citations recorded by the source corpus.
In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title and the PCS workflow that is built on the three principles. The principles will be demonstrated in the context of two collaborative projects in neuroscience and genomics for interpretable data results and testable hypothesis generation.
Subfields: Artificial Intelligence, Biophysics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stefan Bauer
- Gérard Biau
- Bernhard Schölkopf
- Niklas Pfister
- Erwan Scornet
- Sébastien Da Veiga
- Clément Bénard
- Yangfeng Ji
In Biophysics
Citations in the corpus
- Causal Models for Dynamical Systems (2020, methodology): “This finding adds to a recent debate suggesting to add invariance as a fitting criteria to data science methodology [Sch¨olkopf et al., 2012, Yu, 2013, Peters et al., 2016, Bareinboim and Pearl, 2016, Meinshausen et al., 2016, Yu and Kumbier, 2019].”
- Improving the Interpretability of Neural Sentiment Classifiers via Data Augmentation (2019, background): “tion on this text is positive (or negative)? Moreover, the lack of interpretability on model prediction will raise the issue of trustworthy and fairness of sentiment classifiers in practice (Gilpin et al., 2018; Yu and Kumbier, 2019).”
- SIRUS: making random forests interpretable (2019, methodology): “In that case, we argue that it is possible to define minimum requirements for interpretability through the triptych “simplicity, stability, and predictivity”, in line with the framework recently proposed by Yu and Kumbier (2019).”
- Interpretable Random Forests via Rule Extraction (2020, background): “, 2019), it is yet possible to define simplicity, stability, and predictivity as minimum requirements for interpretable models (Bénard et al., 2021; Yu and Kumbier, 2019).”