Interpretable Machine Learning
Decision Tree Learning
Decision trees are one of the leading forms of interpretable machine learning models. They describe how a record’s target feature value (e.g. edibility of a mushroom) depends on the other record’s feature values (e.g. odor, color).
Decision trees:
- solve any kind of problem at hand (classification or regression)
- work with numeric and categorical values
- work with missing values
- are suitable for high stakes decisions
- map nonlinear relationships quite well
We use two types of decision tree learning approaches
- Heuristics-based (iterative tree splitting and pruning process to find tree topology)
- Optimization-based (single process to find tree topology)
Example mushroom dataset with 8124 instances:
Our Decision Tree Learning Experts
Hemlata Sharma
