![]() However, it doesn’t seem like a hindrance because only the best prediction (most voted) would be picked from amongst the possible output classes, thus ensuring smooth, reliable, and flexible executions. There’s a common belief that due to the presence of many trees, this might lead to overfitting. The main advantage of using a Random Forest algorithm is its ability to support both classification and regression.Īs mentioned previously, random forests use many decision trees to give you the right predictions. In supervised learning, the algorithm is trained with labeled data that guides you through the training process. ![]() Random Forest is a Supervised Machine Learning classification algorithm.
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