Naive Bayes classification is a supervised learning algorithm used in the field of machine learning for data classification. It is based on Bayes' theorem and assumes that all input variables are independent of each other.
In simple terms, the algorithm calculates the probability that an input belongs to each possible class, and selects the class with the highest probability as the final classification.
The "Naive" in its name refers to the assumption of independence of the input variables, which may not be true in practice. Despite this simplifying assumption, Naive Bayes classification is widely used due to its ease of implementation and its ability to handle large data sets with high dimensionality.
Since 2008, several countries have enacted legislation that recognizes the importance of integrating artificial intelligence (AI) into key areas of life [...]
Read More »Big data analytics is the process of analyzing large and complex data sources to uncover trends, patterns, customer behaviors, and other data sources [...]
Read More »Today, consumers of any type of product or service have become demanding. It has been a long time since they were served anything [...]
Read More »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 »