Inline with my earlier blog on “Reinforcement learning Simplified” that I recieved excellent reception, I now have Ensemble learning basics and algorithms explained using analogies. Please don’t forget to share your inputs and valuable feedback.
Ensemble, in general, means a group of things that are usually seen as a whole rather than in terms of the value as against the individual value. Ensembles follow a divide-and-conquer approach used to improve performance.
We will start understanding the specific algorithm with an introduction to the to the famous concept of Wisdom of Crowds.
Wisdom of Crowds
Imperfect judgments when aggregated in a right way result in a collective intelligence, thus in a superior outcome. The wisdom of Crowds is all about this collective intelligence. In general, the term crowd is usually associated with irrationality and the common perception that there is some influence, which sways the behavior of the crowd in the context of mobs and cults. However, the fact is that this need not always be negative and works well when working with collating intellect. The key concept of Wisdom of Crowds is that the decisions made by a group of people are always robust and accurate than those made by individuals. The ensemble learning methods of machine learning methods have exploited this idea effectively to produce efficiency and accuracy in their results. [embed]https://www.datadriveninvestor.com/2019/01/23/which-is-more-promising-data-science-or-software-engineering/[/embed]
The term Wisdom of Crowds was discovered by Galton in 1906. Once he attended a farmer’s fair where there was a contest to guess the weight of an ox that is butchered and dressed. The closest guess won the prize for a total of