This is the first in a series of short blog posts where we explore common varieties of Bias that can beset analytics projects. Bias has serious ramifications for the success of analytics in any organization. Understanding the nature of bias is crucial for understanding the extent of a model’s accuracy. In this first post, we discuss what bias is, why it occurs, and why it matters (a lot).
This post first appeared on Elder Research Data Science & Machine Learning Blog, please read the originial post: here