Random Forests — An Intuitive Understanding
It is intriguing to see how simply and easily a Random Forest can yield extremely useful results. Random Forest is a Supervised Machine Learning algorithm. Examples of other supervised learning algorithms are Linear Regression, Logisitic Regression and Neural Networks.
Random Forests are essentially an ensemble of Decision Trees.
Decision trees are
- easy to create,
- easy to implement and
- easy to interpret
But in practice they don’t prove to be very useful. Nevertheless, to understand Random Forests one must know the basic intuition behind Decision Trees.
In its simplest form a Decision tree is a sequence of choices.
As far as accuracies of prediction go Decision Trees are quite inaccurate. Even one mis-step in the choice of the next node, can lead you to a completely different end. Choosing the right branch instead of the left could lead you to the furthest end of the tree. You would be off by a huge margin!
They work great with the data used to create them a.k.a The Training Set but the perform horribly with previously unseen data. This new data is generally called The Validation Set or The Test Set.
