Lee Hodg

In simple Logistic Regression, we have the cost function   $$\mathcal{L}(a, y) = -yln{(a)} – (1-y)ln{(1-a)}$$ where $a$ is the predicted value and $y$ is the ground-truth label on the training set (${0, 1}$).   Why this function? The intuitive version is that if $y=1$ the second term goes away, and the first term that […]