A Practical Guide to Random Forests in Machine Learning

One of the most important popular algorithms in machine learning is Random Forest, and it is used both for classification and regression problems in machine learning. Random Forest also assures high accuracy most of the time, making it one of the most sought-after classification algorithms. Random Forests are built from multiple Decision Trees. The greater the number of trees, the more powerful and refined the model becomes. Each tree casts a vote, and the final prediction is based on the majority vote, which enhances the model’s robustness. In this article, we’ll dive into the inner workings of a Random Forest and then implement it in Python to get a hands-on experience with this algorithm.