![]() Finally, choose the prediction result that received the most votes as the final prediction result.Voting will be conducted using an average of the decision tree. ![]() For each training set of data, this algorithm will build a decision tree.Choose random samples from a specified data collection or training set.The Random Forest Algorithm's operation is described in the phases that follow: Stability - Stability results from the outcome being determined by a majority vote or averaging.Train test split - There is no need to separate the data into train and test sets in a random forest since the decision tree will never be able to view 30% of the data.This implies that we can create a random forest in machine learning by using the CPU to its fullest extent. Parallelization - Each tree is generated using various data and properties.Immune to the dimensionality curse - Because no tree considers every feature, the feature space is condensed. ![]()
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