Modern machine learning is gaining traction across a wide range of industries. However, depending on the application of these analytical models, they can be subject to a high level of scrutiny, required to explain in detail how the model arrives at its solution. For some modern machine learning algorithms, it can be hard or impossible to explain the calculations while maintaining human intuition for the process. For this reason, these models are considered black box models. In Part 1 of this podcast series, we will examine when and how to use black box solutions to solve complex business issues.
Speaker:
Katherine Taylor, Data Scientist – SAS
Hosted by:
Bill Vorhies, Editorial Director – Data Science Central
