These past months have been filled with uncertainty, we have been living a pandemic caused by Covid-19 that has changed our lives and our daily habits; working every day from home, juggling and balancing our work and personal life. Difficult times often bring out the best in people, and this has been no different in Statio, Stratians have been giving the best version of themselves.
Welcome back to our series on Swarm Intelligence Metaheuristics for Optimization. On this post, we will focus on Particle Swarm Optimization. Recall we define Metaheuristics as a class of optimization algorithms which turn out to be very useful when the function being optimized is non-differentiable or does not have an analytical expression at all.
This post will focus on a class of metaheuristics known as Swarm Intelligence. The most amazing feature of these algorithms is their ability to solve complex problems by a set of cooperative agents posing very simple intelligence.
This is the second (and last) part of the series dealing with the formal comparison of Machine Learning (ML) algorithms from a statistical point of view. In this post, we examine how statistical tests are applied to performance data of ML algorithms.
In industry, when a practitioner (often a Data Scientist) uses a machine learning algorithm to build a predictive model to solve a real-world problem, they are interested in the performance when the model is deployed into a production environment…