Why is bias versus variance important for machine learning?

Understanding the terms "bias" and "variance" in machine learning helps engineers to more fully calibrate machine learning systems to serve their intended purposes. Bias versus variance is important because it helps manage some of the trade-offs in machine learning projects that determine how effective a given system can be for enterprise use or other purposes.

In explaining bias versus variance, it's important to note that both of these issues can compromise data results in very different ways.

Bias can be described as a problem that results in inaccurate clusters – it's a situation where machine learning may return many results with precision, but miss the mark in terms of accuracy. By contrast, variance is a "dispersal" of information – it's a wildness, a data that shows a range of results, some of which may be accurate, but many of which will fall outside a particular zone of precision to make the overall result less accurate and much more "noisy."

In fact, some experts describing variance explain that variant results tend to "follow the noise," where high biased results don't go far enough to explore data sets. This is another way to contrast the problem of bias with the problem of variance – experts associate bias with underfitting, where the system may not be flexible enough to include a set of optimal results. By contrast, variance would be a kind of opposite – where overfitting makes the system too fragile and delicate to withstand a lot of dynamic change. By looking at bias versus variance through this lens of complexity, engineers can think about how to optimize the fitting of a system to make it not too complex, not too simple, but just complex enough.

These are two ways that the philosophy of bias versus variance is useful in designing machine learning systems. It's always important to work with machine bias to try to get an overall set of results that are accurate for the use that they are applied to. It's also always important to look at variance in trying to control the chaos or wildness of highly scattered or dispersed results, and to deal with noise in any given system.