Where the Error Lives

scikit-learn decomposes the expected mean squared error of an estimator at a fixed query point as

$\text{Err} = \text{Bias}^2 + \text{Variance} + \text{Noise}$

Refitting a degree-1 model on many fresh samples of the same problem gives $\text{Err} = 0.42$, $\text{Variance} = 0.02$ and $\text{Noise} = 0.05$.

Numerically, $\text{Bias}^2 =$ _____ (two decimal places, written as 0.NN).