randomized decision tree

A randomized decision tree is a machine learning tool that helps make predictions by splitting data into groups based on certain features, similar to a flowchart. Unlike traditional trees that split based on the most precise rule, randomness introduces variability by selecting features or thresholds at random for each split. This approach reduces overfitting—where the model becomes too tailored to specific data—and improves generalization to new data. Multiple randomized trees are often combined (ensemble) to enhance accuracy, creating powerful models like Random Forests that are effective across various tasks.