Evaluating MASHAP as a faster alternative to LIME for model-agnostic machine learning interpretability

Messalas, Andreas; Aridas, Christos K.; Kanellopoulos, Yannis

doi:10.1109/bigdata50022.2020.9378034

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…In 2016, Lundberg et al proposed the SHAP method, which replaced the method of weighting samples according to their proximity to the original instance in LIME with the method of weighting the samples according to the weights obtained by the alliance in the Shapley value estimation. In 2020, Messalas et al proposed a MASHAP method [30]. It first builds a global proxy model on the interested instance, then transfers the proxy model as an original model to the Tree SHAP method, and then generates an explanation.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing

Chen,

Lian,

2023

Axioms

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In the explainable artificial intelligence (XAI) field, an algorithm or a tool can help people understand how a model makes a decision. And this can help to select important features to reduce computational costs to realize high-performance computing. But existing methods are usually used to visualize important features or highlight active neurons, and few of them show the importance of relationships between features. In recent years, some methods based on a white-box approach have taken relationships between features into account, but most of them can only work on some specific models. Although methods based on a black-box approach can solve the above problems, most of them can only be applied to tabular data or text data instead of image data. To solve these problems, we propose a local interpretable model-agnostic explanation approach based on feature relationships. This approach combines the relationships between features into the interpretation process and then visualizes the interpretation results. Finally, this paper conducts a lot of experiments to evaluate the correctness of relationships between features and evaluates this XAI method in terms of accuracy, fidelity, and consistency.

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Section: Related Workmentioning

confidence: 99%

Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing

Chen,

Lian,

2023

Axioms

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“…LIME is a model-independent explanation approach that uses a local (explainable) intermediary model to explain any machine learning model [41].…”

Section: Local Interpretable Model-agnostic Explanations (Lime)mentioning

confidence: 99%

Yield Response of Different Rice Ecotypes to Meteorological, Agro‐Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting and Support Vector Regression

et al. 2022

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The food security of more than half of the world’s population depends on rice production which is one of the key objectives of precision agriculture. The traditional rice almanac used astronomical and climate factors to estimate yield response. However, this research integrated meteorological, agro-chemical, and soil physiographic factors for yield response prediction. Besides, the impact of those factors on the production of three major rice ecotypes has also been studied in this research. Moreover, this study found a different set of those factors with respect to the yield response of different rice ecotypes. Machine learning algorithms named Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) have been used for predicting the yield response. The SVR shows better results than XGBoost for predicting the yield of the Aus rice ecotype, whereas XGBoost performs better for forecasting the yield of the Aman and Boro rice ecotypes. The result shows that the root mean squared error (RMSE) of three different ecotypes are in between 9.38% and 24.37% and that of R-squared values are between 89.74% and 99.13% on two different machine learning algorithms. Moreover, the explainability of the models is also shown in this study with the help of the explainable artificial intelligence (XAI) model called Local Interpretable Model-Agnostic Explanations (LIME).

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“…However, no algorithmic improvements have been made. In [23,24], MASHAP was proposed to compute SHAP values for arbitrary models in an efficient way, where an arbitrary model is first approximated by a surrogate XGBoost model, and TreeSHAP is then applied on this surrogate model to calculate SHAP values. The most related work of improving computational efficiency in TreeSHAP as far as we know is [25], where the authors presented GPUTreeShap as a GPU implementation of TreeSHAP algorithm.…”

Section: Related Workmentioning

confidence: 99%

Fast TreeSHAP: Accelerating SHAP Value Computation for Trees

Yang¹

2021

Preprint

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SHAP (SHapley Additive exPlanation) values are one of the leading tools for interpreting machine learning models, with strong theoretical guarantees (consistency, local accuracy) and a wide availability of implementations and use cases. Even though computing SHAP values takes exponential time in general, TreeSHAP takes polynomial time on tree-based models. While the speedup is significant, TreeSHAP can still dominate the computation time of industry-level machine learning solutions on datasets with millions or more entries, causing delays in post-hoc model diagnosis and interpretation service. In this paper we present two new algorithms, Fast TreeSHAP v1 and v2, designed to improve the computational efficiency of TreeSHAP for large datasets. We empirically find that Fast TreeSHAP v1 is 1.5x faster than TreeSHAP while keeping the memory cost unchanged. Similarly, Fast TreeSHAP v2 is 2.5x faster than TreeSHAP, at the cost of a slightly higher memory usage, thanks to the pre-computation of expensive TreeSHAP steps. We also show that Fast TreeSHAP v2 is well-suited for multi-time model interpretations, resulting in as high as 3x faster explanation of newly incoming samples.

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Evaluating MASHAP as a faster alternative to LIME for model-agnostic machine learning interpretability

Cited by 7 publications

References 8 publications

Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing

Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing

Yield Response of Different Rice Ecotypes to Meteorological, Agro‐Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting and Support Vector Regression

Fast TreeSHAP: Accelerating SHAP Value Computation for Trees

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