The coefficient of determination (also depicted as R^2) is a calculation of the proportion of the variance in the dependent variable (noted as y) which is explained by a linear regression model. The value of the coefficient of determination can be in the 0..1 range. The coefficient of determination can be calculated by using the ... Read more
A convex function is a function which features a single global minimum, whereas a non-convex functions presents many local minima. A good analysis on the mathematical aspects and mathematical definition of convex and non-convex functions can be found at: https://rumn.medium.com/convex-vs-non-convex-functions-why-it-matters-in-optimization-for-machine-learning-39cd9427dfcc. Some examples of ML algorithms which have restrictions related to convex functions are the following: ... Read more
The loss function (or sometimes called error function) is a function which compares the output of an ML model as compared to the ground truth for a single training example, while the cost function is meant to be calculated over the entire training set (or mini-batch for mini-batch gradient descent).
