Linear Regression, Gradient Descent, Machine Learning, Mathematics, Data Analysis.
between each actual y values and the predicted values (h(x) - y) . Each of those differences are then squared to get rid of negatives (h(x) — y)², and then added to equal a single scalar “cost”. That cost is then averaged (1/2m). This final scalar is how we evaluate how accurate our prediction