Random Forest is a popular machine learning algorithm used for both classification and regression tasks. It is a type of ensemble learning method that combines multiple decision trees to make more accurate predictions.
In a Random Forest, each decision tree is trained on a subset of the data and a subset of the features. The final prediction is obtained by averaging the predictions of all the individual trees. This method helps to reduce overfitting and improve the overall performance of the model.
Although the concept of a Random Forest may seem complex, it can be easily understood through a fun and memorable story. In the following story, the concept of a Random Forest is illustrated using animals in a forest to make the idea more accessible to people of all ages.
This article was written in German, automatically translated into other languages and editorially reviewed. We welcome feedback at the end of the article.
How to explain the Random Forest to your children
Once upon a time in a magic forest there were many different plants on which different fruits grew.
A wise old wizard wanted to find out which plants bear the most delicious fruits. He asked his friends, the forest animals, to help him.
Each animal in the forest had its own special ability to taste berries, such as the hare, which can taste any sweet even better than humans, or the fox, which could perceive the sour particularly well.
Then the wizard devised a plan to find out which plant would bear the most delicious fruit. Because the wizard knew that some forest animals are biased and could give a higher score to their favorite fruits.
So the wizard makes it very difficult for the forest animals. He blindfolded some of them so that they could not see the color of the fruit and could only taste it. Others he put a clamp on the nose, so that they do not smell the fruit, but only see the color. He even told some forest animals how old the plant is from which he picked the fruit and how long the sun shines on the plant every day.
Each forest animal tasted the fruits and rated them. To do this, each animal used its special ability to taste the berries and give them a rating, from "oh dear yuck" to "very tasty"!
Then the wizard looked at all the scores and chose the plants that received the highest score from most of the forest animals.
In this way, the wizard could find the plants that produced the tastiest fruit without the bias of a forest animal affecting the results.
And so the animals of the enchanted forest could enjoy delicious fruits all year round, thanks to their powerful wizard!
Put simply, this is how random forests work in machine learning - by forming multiple "opinions" of decision makers and combining their decisions to get a more accurate prediction.