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…
The last chapter of the three-part series on Evolutionary Feature Selection with Big datasets. We will address some fundamental design aspects of a Genetic Algorithm (GA) and commonly chosen options, to then move on to the CHC algorithm and a distributed approach for Feature Selection.
This is the second chapter of a three-part series on Evolutionary Feature Selection with Big datasets. We will start where we left off, namely with a review of existing metaheuristics with special focus on Genetic Algorithms.
Deep learning applications are now truly amazing, ranging from image detection to natural language processing (for example, automatic translation). It gets even more amazing when Deep Learning becomes unsupervised or is able to generate self-representations of the data.