Seeing the Forest for the Trees

The article explores decision trees, random forests, and their applications in machine learning. Decision trees are simple structures that pose questions to split data, while random forests combine multiple decision trees for more accurate predictions. Random forests have advantages like improved accuracy and feature importance Show more