Have you ever wanted to develop a better intuition for measuring the performance of a binary classifier? Precision, recall, accuracy, specificity, F1… Now you have all these metrics under your fingers in the Performance Metrics Playground. You can control your population parameters – number of positive and negative samples, as well as the simulated classifier parameters – number of true positives and true negatives.
Reverse Polish Notation is a method of notation of mathematical expressions that allows simple calculations to be performed without the need of using brackets – thanks to the use of a stack. This method has been popularized by Hewlett Packard, which has been successfully using it in its calculators for many years.
In the previous part we made look through the distribution of sample means for three distributions: Uniform, Cauchy, and Petersburg distribution. The Cauchy and Petersburg distributions do not fulfill the Central Limit Theorem since they have infinite variance (and infinite expected value in “Petersburg” case). Now we will have a look at the numerical results for standard deviation of sample means. As in previous part, we use Uniform distribution only as a reference since it fulfills CLT and we use the same pseud-random number generator (Mersenne-Twister).