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.
The fad coming from the USA that will force the incorporation of AI in the process Surely it is only recently that we have started to hear a new concept in [...]
Read More »Leading AI applications such as most apps are within the reach of many companies and allow large amounts of data to be analyzed and analyzed in a very [...]
Read More »In order to identify the customer's needs, it is necessary to know their opinion, as this helps to detect where you should improve, what acceptance you [...]
Read More »The Official Chamber of Commerce of Seville, in collaboration with the Spanish Institute of Financial Analysts (IEAF), offered last March 16th [...]
Read More »
