covariance

Efficient Feature Selection via CMA-ES (Covariance Matrix Adaptation Evolution Strategy)Using evolutionary algorithms for fast feature selection with large datasetsThis is part 1 of a two-part series about feature selection. Read part 2 here.When you’re fitting a model to a dataset, you may need to perform feature selection: keeping only some subset of the features to fit the model, while discarding the rest. This can be necessary for a variety of reasons:to keep the model explainable (having too many features makes…

A gentle introduction to some very common measures of associationPhoto by Richard Horvath on UnsplashIntroductionUnderstanding associations between variables is crucial for building accurate models and making informed decisions. Statistics can be a messy business; full of noise and random variation. Yet, by identifying the patterns and connections between variables, we can draw insights into how varying features influence each other. For the data scientist and data analyst, such associations are exceedingly useful,…

What is correlation? How can we compute correlation between to continuous variables? And what are the differences with covariance?Clearly, two persons who understood what is correlation. Photo by Jill Wellington: https://www.pexels.com/it-it/foto/due-persone-in-piedi-nella-fotografia-di-sagoma-40815/Machine learning is a wonderful field of study. Studying Machine Learning means taking the most interesting concepts coming from the most disparate fields (math, finance, biology, computer science, etc) with the aim of…