Consider the Ridge LRR is being used to learn prediction function
\( y_{\text{pred}} = w^T x \) where \( w, x \in \mathbb{R}^2 \)
& mean absolute error (MAE) is used to measure the prediction error.
A weight of 0.20 is associated with the regularizer.
At an intermediate step of training process assume that the parameter
\( w = [-3.00, 4.00]^T \). In the next step for the I/P
\( x = [1.00, 2.00]^T \), the predicted value of \( y \) is noted.
Let the relation b/w \( x = [x_1, x_2]^T \) & the true value of
\( y \) be \( y_{\text{true}} = x_1 + x_2 \).
The value of the overall regularized loss for instance is __________ (upto 2 decimal).