Knowledge Distillation Decision Tree for Unravelling Black-box Machine Learning Models
This addresses the lack of interpretability in widely used black-box models, which is an incremental improvement in explainable AI methods.
The paper tackles the problem of interpreting black-box machine learning models by introducing the knowledge distillation decision tree (KDDT) method, which distills knowledge from black-box models into decision trees, with simulation and real-data analysis validating its accuracy and reliability.
Machine learning models, particularly the black-box models, are widely favored for their outstanding predictive capabilities. However, they often face scrutiny and criticism due to the lack of interpretability. Paradoxically, their strong predictive capabilities may indicate a deep understanding of the underlying data, implying significant potential for interpretation. Leveraging the emerging concept of knowledge distillation, we introduce the method of knowledge distillation decision tree (KDDT). This method enables the distillation of knowledge about the data from a black-box model into a decision tree, thereby facilitating the interpretation of the black-box model. Essential attributes for a good interpretable model include simplicity, stability, and predictivity. The primary challenge of constructing interpretable tree lies in ensuring structural stability under the randomness of the training data. KDDT is developed with the theoretical foundations demonstrating that structure stability can be achieved under mild assumptions. Furthermore, we propose the hybrid KDDT to achieve both simplicity and predictivity. An efficient algorithm is provided for constructing the hybrid KDDT. Simulation studies and a real-data analysis validate the hybrid KDDT's capability to deliver accurate and reliable interpretations. KDDT is an excellent interpretable model with great potential for practical applications.