In this video tutorial, we've explained the Random Forest Algorithm with visualizations. You'll also learn why the random forest is more robust than decision trees.
Random forest is an ensemble learning algorithm that consists of many decision trees. The idea is to combine the predictions of many decision trees to get a more accurate prediction than any individual tree could provide.
Each decision tree in a random forest is trained on a different subset of the training data, and each tree is allowed to make mistakes. The predictions of the individual trees are then combined using a voting scheme to get the final prediction.
Random forests are a very powerful machine learning algorithm that can be used for both classification and regression tasks. They are often used for tasks such as spam filtering, image classification, and fraud detection.
