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Random forest is a highly flexible and simple to use machine learning algorithm that produces accurate results. Moreover, it is a widely used algorithm due to its simplicity and diversity. This learning method would use different decision trees. The final decision would be taken based on the majority of the trees that are chosen by the random forest algorithm. Similar to the other machine learning techniques, even the random forests would make use of the training data to predict the future outcome. The random forest would make use of bagging where each tree would learn from the training data. The forest would have thousands of trees.
The reasons to use random forest include:
The random forest is the supervised learning algorithm and is built with a large number of decision trees that are trained with the help of the bagging method. This method is a blend of different learning models that would give you an accurate result. The random forest would let you construct multiple decision trees that are merged together to give an accurate prediction. The best thing about the random forest is that it would solve classification and regression problems briskly.
The random forest would possess the same hyperparameters as the decision tree or the bagging classifier. However, there is no need for you to blend the decision tree with the bagging classifier as it becomes easier for you to use the classifier-class random forest. With the random forest, it becomes easier for you to complete the regression tasks with the help of an algorithm regressor. There is a randomness that is added to the model with the growth of the tree. In the random forest, the features of the random subset would be considered by the algorithm to split the node. You can make the trees random with the help of random thresholds.
The best way to explain the working of random forests is through an example. James is the person who would like to go on a vacation every year. He takes suggestions from his friends and known ones. The first friend whom he approaches would ask about his likes and dislikes based on past travel experiences. With the answers provided by James, the friend would give some valuable advice on travel. The friend has made use of the typical decision tree approach. The friend of James has set the rules to help him with the right decisions based on the answers received from James. Later, James consulted many other friends to take their suggestions on his next travel plan. They have asked him different questions. Finally, James has chosen the destination that is mostly recommended by all of his friends.
There is another example that can better explain the working of a random forest. There are thousands of random trees that are created for which there is a hand. Every random tree would anticipate a different outcome for the same features. The small portion of the forest would take a close look at the random features such as hands or fingers. The randomly picked decision trees would give you some unique features about thumb, fingers, and humans. The votes that you get for the finger would be calculated based on random decisions. The same thing is applicable to the thumb and humans. If the number of votes for the finger is higher, then the predicted target would return as the finger.
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Students who are pursuing machine learning courses must be thorough with this topic. Here are a few applications where the random forest algorithm would be used
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