Variance is the amount that the estimate of the target function will change if different training data was used.
n reality, we cannot calculate the real bias and variance error terms because we do not know the actual underlying target function. Nevertheless, as a framework, bias and variance provide the tools to understand the behavior of machine learning algorithms in the pursuit of predictive performance.
There is no escaping the relationship between bias and variance in machine learning.
- Increasing the bias will decrease the variance.
- Increasing the variance will decrease bias.
There is a trade-off at play between these two concerns and the algorithms you choose and the way you choose to configure them are finding different balances in this trade-off for your problem