How did one statistician's 'Random Forests' algorithm grow into one of the most reliable tools in machine learning?

The answer

In 2001, UC Berkeley statistician Leo Breiman published his landmark paper introducing Random Forests, an ensemble method that builds hundreds or thousands of decision trees on random subsets of data and features, then aggregates their predictions by majority vote. The paper has been cited over 100,000 times, making it one of the most influential machine learning papers ever written. Random Forests became beloved for their resistance to overfitting, ability to handle missing data, and requiring minimal hyperparameter tuning, making them a default choice in fields from genomics to finance.