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.
In the following article, we will look at image recognition using linear regression. We realize that this idea may seem quite unusual. However, we will show using a simple example, that for a certain class of images, and under quite strictly defined circumstances, the linear regression method can achieve surprisingly fair results.
When we want to know a standard deviation of a big population, we usually take a sample from the whole and than calculate estimator value. However it is not always clear which estimator should we use. Sometimes people argue whenever biased or unbiased standard deviation estimator is better. Below we explore this field and present the result of the numerical simulation.
The power of Central Limit Theorem is widely known. In the following post we are exploring a bit the areas outside its scope – where the CLT does not work. We present the results of numerical simulations for three distributions: Uniform, Cauchy distribution, and certain “naughty” distribution called later “Petersburg distribution”.