Landscape of R packages for eXplainable Artificial Intelligence

Maksymiuk, Szymon; Gosiewska, Alicja; Biecek, Przemyslaw

doi:10.48550/arxiv.2009.13248

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…Our approach focuses on the efficient use of large volumes of elevation data to find hydrological analogues through dynamical properties of terrains and facilitates large scale applications. This approach is consistent with the growing recognition in the hydrological community regarding the use of explainable AI (XAI) techniques that build upon conceptual and machine learning models to explain hydrological phenomenon (Maksymiuk et al, 2020;Althoff et al, 2021). An application of hydrological similarity study is to assist in improving our understanding of hydrological processes in watersheds (Blöschl et al, 2013) and future works can build upon this study by integrating the width function and elevation-based slope and velocity distribution to create a robust dynamical metric for hydrological response quantification and similarity assessment.…”

Section: Discussion and Concluding Remarkssupporting

confidence: 76%

Hydrologic similarity based on width function and hypsometry: An unsupervised learning approach

Bajracharya

Jain

2022

Computers & Geosciences

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Section: Discussion and Concluding Remarkssupporting

confidence: 76%

Hydrologic similarity based on width function and hypsometry: An unsupervised learning approach

Bajracharya

Jain

2022

Computers & Geosciences

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“…Only the top fifteen important variables were displayed in the variable importance plot. The explainable approach was applied using DALEX and DALEXtra packages versions 2.4.2 and 2.2.1 [ 51 , 52 ].…”

Section: Methodsmentioning

confidence: 99%

Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records

et al. 2022

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This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.

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“…All the utilized methods were implemented using the open-source R statistical computing environment with the following packages: random-Forest (Breiman, 2001) for classification and regression based on a forest of trees using random inputs, caret (Kuhn, 2015) for data splitting and generating stratified bootstrap samples, and DALEX (Maksymiuk et al, 2020) for variable importance and partial dependence.…”

Section: Rf Model Developmentmentioning

confidence: 99%

What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China

Zhang

Guo

Yang

et al. 2022

J of Industrial Ecology

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Material intensity coefficient (MIC) is vital for material stock accounting in the field of industrial ecology. However, the categorization of MIC varies across regions especially for buildings that diverge greatly along the history and space aspect, and acquisition of MIC data and building information have always been a challenge in related studies. In this study, the state-of-art ensemble model "Random Forest" was developed on Chinese buildings to identify the impact of four building attributes (building structure, construction year, use type, and region) on MIC, and these features' importance was further assessed by considering variable correlations. The features' importance and their individual effects on MIC were intuitively revealed by depicting the partial dependence plots. Finally, a set of hierarchical MIC values was estimated by integrating the order of four variables' importance and a quick MIC calculator was provided. Resultsshowed that building structure is the most influential attribute for MIC, followed by the construction year, use type, and region, successively. The RF-based MIC values allow researchers to apply it to material stock and flow analysis by choosing a specific building feature(s) in the MIC calculator, which is (are) available in building physical inventory data. This study provides a method that could help researchers locate key influencing variables and give insights into the comparability of MIC research across regions and play an important role in developing urban mining and circular economy strategies.

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Landscape of R packages for eXplainable Artificial Intelligence

Cited by 12 publications

References 27 publications

Hydrologic similarity based on width function and hypsometry: An unsupervised learning approach

Hydrologic similarity based on width function and hypsometry: An unsupervised learning approach

Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records

What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China

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