What is random forest in simple terms?
Artificial Intelligence & Machine Learning
Random Forest is a popular machine learning algorithm that was first introduced by Leo Breiman and Adele Cutler in 2001. The algorithm was initially designed for classification problems, but it can also be used for regression. Super useful for a wide variety of applications.
The idea behind Random Forest is based on decision tree algorithms. Decision trees are simple, easy-to-understand models. Models that really work well for many problems.
However, they can easily become unstable and they are definitely very prone to overfitting.
Yet, we find that Random Forest overcomes these limitations. As a result of using an ensemble of decision trees!
Where each tree is trained on a random subset of the data and a random subset of the features.
The history of Random Forest can be traced back to the work of Leo Breiman, who was a professor of statistics at the University of California, Berkeley.
In 1996, Breiman introduced a new algorithm called Bagging, which stands for Bootstrap Aggregating. Bagging involves training multiple models on different subsets of the training data. In addition aggregating their predictions to improve the overall accuracy of the model. Random Forest is a variant of Bagging that uses decision trees as the base models.
Breiman and Cutler introduced Random Forest in their 2001 paper “Random Forests,” which became a seminal work in the field of machine learning. Since then, Random Forest has become one of the most popular machine learning algorithms, and it has been applied to a wide range of problems, including image classification, text classification, and financial forecasting.
In conclusion, Random Forest is a powerful machine learning algorithm!
A history we can trace back to the work of Leo Breiman. Who introduced the Bagging algorithm in 1996. And lastly, its development was continued by Breiman and Cutler in their 2001 paper.
Artificial Intelligence & Machine Learning
What is random forest in simple terms?
