A beginner-friendly introduction to Decision Trees as supervised machine learning algorithms for classification and regression. Covers how trees are built by recursively partitioning feature space, explains entropy as a measure of data impurity, and details the ID3 algorithm which uses information gain to select optimal splits.
Table of contents
MLU-expl AI nLet's Build a Decision TreeStart SplittingSplit Some MoreAnd Some MoreAnd Yet Some MoreDon't Go Too Deep!Where To Partition?Sort: