Random forest builds multiple decision trees using different subsets of the data and averages their predictions. Here is a step-by-step process of how it operates:
1. Data Sampling: Random subsets of the data are created with replacement (bootstrap sampling). 2. Tree Construction: Each subset is used to build a decision tree. 3. Feature Selection: At each node in the tree, a random subset of features is selected to determine the best split. 4. Aggregation: The predictions from all trees are aggregated (majority voting for classification, mean for regression).
