  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. We present a paradigm for
ensemble modeling which mitigates the need to build complicated preprocessing
and encoding schemes for categorical variables. In particular, domain-specific
conjugate Bayesian models are employed as base learners for features in a
stacked ensemble model. For each column of a categorical feature matrix we fit
a problem-specific prior distribution, for example, the Beta distribution for a
binary classification problem. In order to analytically derive the moments of
the posterior distribution, we update the prior with the conjugate likelihood
of the corresponding target variable for each unique value of the given
categorical feature. This function of column and value encodes the categorical
feature matrix so that the final learner in the ensemble model ingests
low-dimensional numerical input. Experimental results on both curated and real
world datasets demonstrate impressive accuracy and computational efficiency on
a variety of problem archetypes. Particularly, for the lead scoring engine at
WeWork -- where some categorical features have as many as 300,000 levels -- we
have seen an AUC improvement from 0.87 to 0.97 through implementing conjugate
Bayesian model encoding.
