In this tutorial, we are going to learn an advanced concept related to decision trees called random forests. The idea behind random forests is the concept of ensemble learning. Sir Francis Galton (1822–1911), an English philosopher and statistician, was the brain behind the basic ideas of standard deviation and correlation. Once, during a visit to a livestock fair, Galton got interested in a simple game where people tried to guess the weight of an ox. Lots of folks joined in, but no one hit the exact weight: 1,198 pounds.
But guess what? Galton discovered something cool - when he averaged out all the guesses, it was super close to the real weight: 1,197 pounds. This reminded him of the Condorcet jury theorem, showing that combining many simple guesses can give a really good result. Fast forward to 2004, an American financial journalist named James Michael Surowiecki wrote a book called "The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations." Surowiecki's idea is pretty neat - when you gather info from different people, you can make better decisions, sometimes even better than what a super-smart person might decide. This is the basic idea of ensemble learning in machine learning.
An ensemble in the context of machine learning refers to the technique of combining multiple individual models to create a stronger, more robust predictive model. The idea behind ensemble methods is to leverage the diversity of multiple models to improve overall performance, generalization, and accuracy. A group of predictors is called an ensemble and an ensemble Learning algorithm is called an Ensemble method.
Now the question is how do we ensemble models? What are the combination methods? Where does random forest fit in this whole thing? That's what we are going to learn in this tutorial.
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