Complex decision tree ensembles like random forests and gradient-boosted trees can be hard to understand and interpret. This results in difficulties for data scientists in making informed decisions about model refinement or deployment.
TimberTrek helps address this issue by providing an interactive visualization tool for exploring and comparing multiple decision tree models.
It also lets users filter and select models based on custom criteria (e.g., fairness, simplicity).
