Random Forest
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Decision Tree Algorithm o Supervised machine learning algorithms. o It is used for both a classification problem as well as for regression problem. o It predicts the value of a target variable(dependent variable) o A decision tree represents a procedure for classifying categorical data based on their attributes. o The construction of decision tree does not require any domain knowledge or parameter setting, and therefore appropriate for exploratory knowledge discovery. o Decision tree uses the tree representation to solve the problem in which the leaf node corresponds to a class label and attributes are represented on the internal node of the tree Decision Tree learning is one of the most widely used and practical methods for inductive inference over supervised data. o Their representation of acquired knowledge in tree form is intuitive and easy to assimilate by humans. Decision Tree Classification Example 1 Ø In above diagram leaf node