Forestry

Random forest

Incremental decision tree

Set (abstract data type)

Data mining

Computer science

Alternating decision tree

Mathematics

Geography

Decision tree learning

Mathematical analysis

Path (computing)

Artificial intelligence

Decision tree

Tree (set theory)

Programming language

Decision rule

Machine learning

Information Fusion

Decision forests are considered the best practice in many machine learning challenges, mainly due to their superior predictive performance. However, simple models like decision trees may be preferred over decision forests in cases in which the generated predictions must be efficient or interpretable (e.g. in insurance or health-related use cases). This paper presents a novel method for transforming a decision forest into an interpretable decision tree, which aims at preserving the predictive performance of decision forests while enabling efficient classifications that can be understood by humans. This is done by creating a set of rule conjunctions that represent the original decision forest; the conjunctions are then hierarchically organized to form a new decision tree. We evaluate the proposed method on 33 UCI datasets and show that the resulting model usually approximates the ROC AUC gained by random forest while providing an interpretable decision path for each classification.

Explainable decision forest: Transforming a decision forest into an interpretable tree