Interpreting Tree Ensembles with inTrees (1408.5456v1)

Abstract: Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also be formed and used for future prediction. The inTrees framework can applied to both classification and regression problems, and is applicable to many types of tree ensembles, e.g., random forests, regularized random forests, and boosted trees. We implemented the inTrees algorithms in the "inTrees" R package.

• The paper introduces the inTrees framework to extract clear, actionable rules from complex tree ensembles.
• It systematically measures, prunes, and selects rules, reducing model complexity while preserving predictive accuracy.
• Empirical analysis on 20 datasets shows that the simplified STEL model often outperforms traditional decision tree methods.

Interpreting Tree Ensembles with inTrees

The paper "Interpreting Tree Ensembles with inTrees" by Houtao Deng introduces a framework aimed at addressing one of the persistent challenges in the deployment of tree ensembles like random forests and boosted trees: interpretability. The inTrees framework provides a systematic approach to extract, process, and select rules from tree ensembles, thus enabling models that are not only accurate but also interpretable and practical for application.

Tree ensembles have long been regarded for their high predictive accuracy in various supervised learning tasks, whether regression or classification. However, their complex structure and the large number of trees involved pose specific challenges when it comes to understanding, debugging, and deploying these models. The inTrees framework tackles these challenges by offering rule extraction, measurement, pruning, and selection techniques to simplify these models while preserving their predictive power.

inTrees Framework

The inTrees framework is designed to work with a plethora of tree structures outside of assumptions imposed by the building process, enabling the transformation of any tree ensemble into a simplified rule-based representation. The extracted rules stem from the paths within the decision trees that culminate at leaf nodes, providing conditions that must be met for a particular prediction to occur.

Key components of the inTrees framework include:

• Rule Extraction: This process involves the derivation of rules framed as {condition} ⇒ {outcome} pairs. They are obtained by traversing each decision tree in the ensemble from the root to the leaf nodes.
• Rule Measurement: Rules are assessed based on frequency, error, and complexity. Frequency conveys how often a rule is applicable, error measures predictive inaccuracies, and complexity reflects the intricacy involved in a rule's condition.
• Rule Pruning: During pruning, redundant or irrelevant condition parts are removed to enhance interpretability and efficiency without compromising accuracy.
• Rule Selection: Utilizing techniques derived from feature selection principles, this step seeks to condense the rule set to its most essential, forming a rule-based learner referred to as the Simplified Tree Ensemble Learner (STEL).

Implementation and Results

The inTrees framework is implemented as an R package, epitomizing the flexibility and versatile application of the framework. It caters to several kinds of tree ensembles, notably random forests and regularized random forests.

Empirical results from experiments with 20 UCI datasets demonstrate inTrees' capability to generate simplified, interpretable models with competitive accuracy. In 13 out of 20 datasets, the STEL model outperformed traditional decision tree methods like those in the rpart package, indicating a significant reduction in prediction errors.

Implications and Future Directions

The inTrees framework's meticulous approach to rule-based interpretation of tree ensembles holds considerable implications for both practice and theory. Practically, it enables deployment in environments requiring model transparency and simplicity, which is invaluable in fields like finance and healthcare. Theoretically, this work provides a conduit for traditional rule-mining techniques to be leveraged in complex machine learning models, opening avenues for further exploration and refinement.

Future developments may focus on extending the application of the inTrees framework to encompass more complex, heterogeneous models and coupling them with advanced parallel computing strategies for improved processing efficacy. Additionally, expanding upon the framework’s capacity to handle diversified data types and exploring its integration with other machine learning paradigms may yield richer interpretative insights and cross-domain applicability.