Discover the evolution and importance of decision trees in machine learning, from their early beginnings in the 1960s to their widespread use in modern ensemble - ml decisiontree
Decision trees are a versatile and interpretable machine learning technique, with roots in the early 1960s. Modern decision tree algorithms have evolved to address limitations like overfitting and handling continuous features.
The true potential of decision trees is realized within ensemble methods like Isolation Forests, Random Forests, and Boosted Trees, which improve accuracy, robustness, and generalizability. Understanding these timeless classics is essential for researchers and practitioners in the field.
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