‘Spotify Confidence’: convenience wrappers around stat models various functions
Spotify released Spotify Confidence, which provides
“convenience wrappers around stats model’s various functions for computing p-values and confidence intervals. With Spotify Confidence, it’s easy to compute several p-values and confidence bounds in one go, e.g. one for each country or for each date. Each function comes in two versions, A) one that returns a pandas dataframe, and B) one that returns a Chartify chart.
Spotify Confidence has support calculating p-values and confidence intervals using Z-statistics, Student’s T-statistics (or more exactly Welch’s T-test), as well as Chi-squared statistics.
There is also a Bayesian alternative in the BetaBinomial class.
https://github.com/spotify/confidence
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About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).