Naive Bayes Classification data mining algorithm

Naive Bayes Classification data mining algorithm

Naive Bayes Classification data mining algorithm is used to search for the known patterns of phrases in large databases that contain thousands of unsorted data.

[ Requirements for use of Naive Bayes Algorithm ]

The Naive Bayes algorithm requirements before use include:-

1. A single key column that uniquely identifies each unit of data.
2. Units of data in the database are independent of each other and are only related via classification.
3. The units-of-data’s single key column is predictable due to its uniqueness.
4. Presence of historical data.
5. Presence of a set of data that is already sorted (not necessary as data can be sorted before application of the algorithm)

[ Naive Bayes Algorithm in action ]

The Naive Bayes Algorithm works by calculating the probability of occurrence of events or values. ItBayes Rule utilizes a Bayes Rule to provide a formula to calculate the probability of ‘A‘, given multiple variables, B, C, D, E, etc., which are all assumed to be independent.

Below are the sequential steps that the Naive Bayes Algorithm follows during search of a specific data unit:-

– The algorithm starts by learning from the training data (the known data).
– Next, the algorithm uses known information and the training data to obtain the prior probability of occurrence.
– What follows is computation of probability of likelihood of evidence (possible identifications of a specific data unit from the large set) using a formula that categorizes the data unit.

Naive Bayes Classification data mining algorithm
Wiki | thetqweb