“Study And Analysing of Decision Tree”

Abstract

Decision tree are indeed a form of Absraction in artificial intelligence. They abstract complex decision processes into a hierarchical structure of decisions and their consequences. Each mode in the tree represents a decision based on a particular feature or attribute, and each branch represents the possible outcomes of that decision. This abstraction allows AI systems to efficiently navigate through decision spaces, making them particularly useful in classification and regression task. By recursively partitioning the feature space based on available data, decision trees can learn complex decision boundaries and make prediction or complex classification with relatively simple rules. This abstraction simplifies the problem-solving process and can often lead to interpretable models, where humans can understand and interpret the logic behind the AI’s decisions.