Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees

What this post added

This post details the development of a Bayesian Tree framework for predicting rider conversion. It introduces a hierarchical tree structure for data decomposition and explains the application of Bayesian smoothing with Gaussian priors to handle data sparsity. The post also discusses how the framework enforces behavioral consistency and monotonicity in predictions, contributing a new capability for real-time predictive modeling in sparse environments.