Find constant minimizing absolute error

You have a dataset of real-valued targets \(y_1, y_2, \dots, y_n\). You want to fit a **constant** predictor (no features): - Prediction: \(\hat y_i = c\) for all \(i\) - Objective (L1 loss): \[ E(c) = \sum_{i=1}^n |y_i - c| \] **Task:** Find the value(s) of \(c\) that minimize \(E(c)\). Describe what happens when \(n\) is odd vs. even, and how to compute \(c\) from the data.