Random Naive Bayes and Random Forest
Generalizing Random Forest to Naive Bayes, Random Naive Bayes (Random NB), is a bagged classifier combining a forest of B Naive Bayes. Each bth Naive Bayes is estimated on a bootstrap sample Sb with m randomly selected features. To classify an observation put the input vector down the B Naive Bayes in the forest. Each Naive Bayes generates posterior class probabilities. Unlike Random Forest, the predicted class of the ensemble is assessed by adjusted majority voting rather than majority voting, as each bth Naive Bayes delivers continuous posterior probabilities. Similar to Random Forests, the importance of each feature is estimated on the out-of-bag (oob) data.
Read more about this topic: Random Naive Bayes
Famous quotes containing the words random, naive and/or forest:
“We should stop looking to law to provide the final answer.... Law cannot save us from ourselves.... We have to go out and try to accomplish our goals and resolve disagreements by doing what we think is right. That energy and resourcefulness, not millions of legal cubicles, is what was great about America. Let judgment and personal conviction be important again.”
—Philip K. Howard, U.S. lawyer. The Death of Common Sense: How Law Is Suffocating America, pp. 186-87, Random House (1994)
“The naive notion that a mother naturally acquires the complex skills of childrearing simply because she has given birth now seems as absurd to me as enrolling in a nine-month class in composition and imagining that at the end of the course you are now prepared to begin writing War and Peace.”
—Mary Kay Blakely (20th century)
“This land is your land, this land is my land, From California to the New York Island. From the redwood forest to the Gulf Stream waters This land was made for you and me.”
—Woody Guthrie (1912–1967)