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Course Content


Random forest is a Supervised Machine Learning Algorithm commonly used in classification and regression issues. It constructs decision trees from several samples and uses their majority vote for classification and average for regression.

Random Forest is a decision tree-based supervised machine learning technique. Random Forest is used for classification as well as regression, such as determining if an email is "spam" or "not spam."

The most typical response I receive is that the Random Forest is so named because each tree in the forest is constructed by randomly selecting a sample of the data.

The key distinction between the random forest algorithm and decision trees is that decision trees are graphs that depict all possible outcomes of a decision via a branching strategy. In contrast, the random forest algorithm produces a set of decision trees that function based on the output.

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