How Random Forest Algorithm Works in Machine Learning

6 min readOct 24, 2017

This is one of the best introductions to Random Forest algorithm. The author introduces the algorithm with a real-life story and then provides applications in four different fields to help beginners learn and know more about this algorithm.

To begin the article, the author highlights one advantage of Random Forest algorithm that excites him: that it can be used for both classification and regression problems. (Random Forest algorithm also have the other advantages, which will be shown at the end of the article). The author chose a classification task for this article, as this will be easier for a beginner to learn. Regression will be the application problem in the next, up-coming article.

This article spans six parts:

  1. What is Random Forest algorithm?
  2. Why Random Forest algorithm?
  3. Random Forest algorithm real life example.
  4. How Random Forest algorithm works?
  5. Random Forest algorithm Application.
  6. Advantages of Random Forest algorithm.

What is Random Forest algorithm?

First, Random Forest algorithm is a supervised classification algorithm. We can see it from its name, which is to create a forest by some way and make it random. There is a direct relationship between the number of trees in the forest and the results it can get: the larger the number of trees, the more accurate the result. But one thing to note is that creating the forest is not the same as constructing the decision with information gain or gain index approach.

The author gives 4 links to help people who are working with decision trees for the first time to learn it, and understand it well. The decision tree is a decision support tool. It uses a tree-like graph to show the possible consequences. If you input a training dataset with targets and features into the decision tree, it will formulate some set of rules. These rules can be used to perform predictions. The author uses one example to illustrate this point: suppose you want to predict whether your daughter will like an animated movie, you should collect the past animated movies she likes, and take some features as the input. Then, through the decision tree algorithm, you can generate the rules. You can then input the features of this movie and see whether it will be liked by your daughter. The process of…


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