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.
In the dynamic financial world, optimizing the return on available assets is essential to the success of any lender. Gam [...]
Read More »There is a broad consensus among executives of the world's leading companies about the impact that artificial intelligence is going to have on business and [...]
Read More »Collecting debts, nowadays, is becoming an arduous task for many companies or freelancers. More and more banks, debt collection [...]
Read More »Natural Language Processing or NLP analyzes how machines understand, interpret and process human language.
Read More »
