Hello all, this is the free Introduction to ML bootcamp series(4/22) In the most well-known form of Machine Learning, i.e Supervised Learning, we intend to come up with some model that can predict labels for our inputs, and we need some performance measure P, hence we invent “Misclassification rate” on the training set. The latter counts the fraction of miss-classified labels, written via an indicator function, which is just a mathematical way to express it. Indicator function assumes all errors are equal, but some misclassification may be more detrimental, for instance if among the flower varieties that we are classifying, one variant happens to be poisonous, which if classified as benign, can be fatal. Hence, the need for an asymmetric loss function. As we measure loss empirically, we define it to be as empirical risk. One way to see model fitting is to minimize the loss on the training set, known as empirical risk minimization, however, this is not really what we want. In reality we want the model to “Generalize”, that is to minimize the expected loss on the future data that we have not yet seen. The premise of Empirical risk minimization assumes that the training distribution is very analogously close to the actual distribution we are sampling from, which when false, creates problems. However, ERM does work for many practical cases, and is a good starting point to understanding how we come up with performance measures in Machine Learning. In the video, I breakdown the mathematics and the equations that describe these phenomena: Link: https://youtu.be/bqv4XC6Arqo submitted by /u/Negative_War_65
Originally posted by u/Negative_War_65 on r/ArtificialInteligence