Why Decision Tree is called Decision Tree? 🌲🎄 Explained in 60 Seconds

Decision tree algorithms are popular in machine learning and data analysis due to their simplicity, interpretability, and effectiveness. Decision trees mimic human decision-making processes. They consist of nodes, branches, and leaves, where nodes represent decisions, branches represent possible outcomes, and leaves represent final predictions.
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It was named so because of its Botanical Inspiration. The term "decision tree" was inspired by its visual resemblance to an upside-down tree, with branches representing decisions and leaves as final outcomes.
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