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.
Artificial intelligence (AI) solutions are valuable in reducing product returns. Through data analysis and decision [...]
Read More »In the previous articles ("Basic concepts to build a commercial software with artificial intelligence" and "How to materialize the opportun [...]
Read More »Industry 4.0 is the name given to the fourth industrial revolution, which is characterized by the inclusion of advanced technologies in production processes.
Read More »Industry 4.0 or the Fourth Industrial Revolution is based on the integration of digital technologies in the production and processing of goods and services.
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