Abstract
Tsetlin Machines (TMs) capture patterns using conjunctive clauses in propositional logic, thus facilitating interpretation. However, recent TM-based approaches mainly rely on inspecting the full range of clauses individually. Such inspection does not necessarily scale to complex prediction problems that require a large number of clauses. In this paper, we propose closed-form expressions for understanding why a TM model makes a specific prediction (local interpretability). Additionally, the expressions capture the most important features of the model overall (global interpretability). We further introduce expressions for measuring the importance of feature value ranges for continuous features making it possible to capture the role of features in real-time as well as during the learning process as the model evolves. We compare our proposed approach against SHAP and state-of-the-art interpretable machine learning techniques.
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Notes
- 1.
We will understand black-box models as models which lack intrinsic interpretability features, such as ensemble approaches, neural networks, and random forests.
- 2.
Any systematic division of clauses can be used as long as the cardinality of the positive and negative polarity sets are equal.
- 3.
The choice of hyperparameters of the IWTM can be summarized as picking the number of clauses randomly three times, between 50 and 500 clauses, with a threshold of twice the number of clauses. The best model in terms of accuracy was chosen of the three configurations.
- 4.
DNN with 10 hidden layers containing 100 units each with ReLU activation and Adam optimizer.
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Blakely, C.D., Granmo, OC. (2021). Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_14
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DOI: https://doi.org/10.1007/978-3-030-79457-6_14
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