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.
Intelligent Process Automation in companies has changed in the world very rapidly in recent years. The COVID-19, the interr [...]
Read More »The use of Artificial Intelligence in business is becoming more and more common and necessary for the optimization and evolution of processes. In one of our [...]
Read More »Chargeback refers to refunds that occur when, at the request of a cardholder, the bank requests a refund on his or her behalf [...].
Read More »Nowadays digital transformation is key in any type of business. The 40% of Spanish companies will not exist in its current form in the next few [...]
Read More »
