Encoding Categorical Variables with Conjugate Bayesian Models for WeWork Lead Scoring Engine

30 Apr 2019 Austin Slakey Daniel Salas Yoni Schamroth

Applied Data Scientists throughout various industries are commonly faced with the challenging task of encoding high-cardinality categorical features into digestible inputs for machine learning algorithms. This paper describes a Bayesian encoding technique developed for WeWork's lead scoring engine which outputs the probability of a person touring one of our office spaces based on interaction, enrichment, and geospatial data... (read more)

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