Feature selection is a process of selecting relevant and informative variables for a machine learning model, with the aim of improving the accuracy and generalisability of the model. Instead of using all available variables, the most relevant features are selected to reduce computational cost and improve model interpretation. Feature selection techniques include statistical, correlation and feature importance methods, among others. It is a technique commonly used in data pre-processing for machine learning.
The current scenario we are experiencing in Spain with the COVID-19 health crisis has led to many companies having to carry out ER [...]
Read More »Artificial intelligence (AI) solutions are valuable in reducing product returns. Through data analysis and decision [...]
Read More »Collecting debts, nowadays, is becoming an arduous task for many companies or freelancers. More and more banks, debt collection [...]
Read More »The integration of tools for predictive analytics is already commonplace in large companies, but thanks to the evolution and, above all, to the dem [...]
Read More »
