Epistemic Uncertainty¶
Preface¶
In the context of machine learning, given predictor \(f\) and outcome \(y\):
\[
f(\mathbf{x}) - y = \underbrace{f(\mathbf{x}) - f_{\text{best}}(\mathbf{x})}_{\text{due to insufficient data}} + \underbrace{f_{\text{best}}(\mathbf{x}) - f^{*}(\mathbf{x})}_{\text{bias}} + \underbrace{f^{*}(\mathbf{x}) - y}_{\text{irreducible error}}
\]
Definition¶
Epistemic Uncertainty is the uncertainty in the model due to the lack of knowledge or data.
In machine learning, this uncertainty corresponds to the reducible error. Which is caused by limited data (variance), and inadequate model structure (bias).