Description Of Naive Bayesian Classifier And Application In Real Life (Essay Sample)

Naïve Bayes Name Institutional Affiliation Author note Naïve Bayes Naive Bayesian Classification is referred to as naïve since it assumes that each of the input is conditionally independent. The assumption rarely holds any truth, and it’s where the term naïve originates. That is, the impact of an attribute on a given class works independently. Research has established that even though the assumption might be false, the approach still performs well hence considered one of the most powerful tools used in the classification process and machine learning. Additionally, the assumption is used in reducing computational costs hence regarded as naïve. The major idea behind the classification approach is to classify data via maximization of the Bayes theorem of probability. The theory uses Bayesian classifier in calculating the posterior probability of a single class label with the maximum posterior probability conditioned in another vector. Naïve Bayes has been well understood as an effective and efficient classification algorithm. However, the approach works by conditional independence which assumes to be ignored in several applications. Unlike other data mining methods, the approach makes use of accurate classification rather than an accurate ranking of the events. For instance, ranking the clients on the likelihood of buying a particular commodity is useful in identifying the market. Other approaches investigate the ranking performance through lazy algorithms that extend to Naive Bayes. The performance of ranking is measured, and the observation fails to improve the Naïve Bayes ranking performance significantly. Motivated by the fa...