[cpb]

In an interview, Ryan Caldbeck from Circle Up describes two categories of models: brainy models and brawny models. The ensemble described above sounds like a brawny model: you don't care how it made the decision, you're glad it did the heavy lifting and you might even double check the result.

However, the user's concern about the black box suggests they wanted what Ryan refers to as a brainy model, one with explicable decisions. Even within the features of the model there could be things to learn about the data.

How else are data scientists stuck within their own world?