gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

Hatwell, Julian; Gaber, Mohamed Medhat; Azad, R. Muhammad Atif

doi:10.3390/app11062511

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…A drawback of this approach is that it lacks a strict restriction about the number of paths that will be covered through the generated rule, particularly in multi-class classification tasks. An extension of CHIRPS to gradient boosted tree ensembles is gbt-HIPS [33].…”

Section: Related Workmentioning

confidence: 99%

Conclusive Local Interpretation Rules for Random Forests

Mollas¹,

Bassiliades²,

Tsoumakas³

2021

Preprint

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In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear interpretations for their decisions. Otherwise, their obscure decision-making processes can lead to socioethical issues as they interfere with people's lives. In the aforementioned sectors, random forest algorithms strive, thus their ability to explain themselves is an obvious requirement. In this paper, we present LionForests, which relies on a preliminary work of ours. LionForests is a random forest-specific interpretation technique, which provides rules as explanations. It is applicable from binary classification tasks to multi-class classification and regression tasks, and it is supported by a stable theoretical background. Experimentation, including sensitivity analysis and comparison with state-of-the-art techniques, is also performed to demonstrate the efficacy of our contribution. Finally, we highlight a unique property of LionForests, called conclusiveness, that provides interpretation validity and distinguishes it from previous techniques.

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Section: Related Workmentioning

confidence: 99%

Conclusive Local Interpretation Rules for Random Forests

Mollas¹,

Bassiliades²,

Tsoumakas³

2021

Preprint

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“…have been applied for modeling surface water salinity as a function of other water quality variables and lake nutrients as a function of watershed characteristics (Wang et al, 2021;Khan et al, 2022). Boosted trees are related to random forests, but each new tree is trained based on the errors of the previous tree (Hatwell et al, 2021). Boosted trees have been applied to predict the flood susceptibility of tracts of land or groundwater well-productivity based on geographic data (Lee et al, 2017(Lee et al, , 2019.…”

Section: Introductionmentioning

confidence: 99%

Detecting industrial discharges at an advanced water reuse facility using online instrumentation and supervised machine learning binary classification

Thompson

Branch²,

Nading

et al. 2022

Front. Water

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Industries occasionally discharge slugs of concentrated pollutants to municipal sewers. These industrial discharges can cause challenges at wastewater treatment plants (WWTPs) and reuse systems. For example, elevated total organic carbon that is refractory through biological wastewater treatment increases the required ozone dose, or even exceeds the capacity of the ozone unit, resulting in a treatment pause or diversion. So, alert systems are necessary for potable reuse. Machine learning has many advantages for alert systems compared to the status quo, fixed thresholds on single variables. In this study, industrial discharges were detected using supervised machine learning and hourly data from sensors within a WWTP and downstream advanced treatment facility for aquifer recharge. Thirty-five different types of machine learning models were screened based on how well they detected an industrial discharge using default tuning parameters. Six models were selected for in-depth evaluation based in their training set accuracy, testing set accuracy, or event sensitivity: Boosted Tree, Cost-Sensitive C5.0, Oblique Random Forest with Support Vector Machines, penalized logistic regression, Random Forest Rule-Based Model, and Support Vector Machines with Radial Basis Function Kernel. After optimizing the tuning parameters and variable selection, Boosted Tree had the highest testing set accuracy, 99.2%. Over the 5-day testing set, it had zero false positives and would have detected the industrial discharge in 1 h. However, setting fixed thresholds based on the maximum normal datapoint within the training set resulted in nearly as good testing set accuracy, 98.3%. Overall, this study was a successful desktop proof-of-concept for a machine learning-based alert system for potable reuse.

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“…To solve this deficiency, multiple strategies are proposed in a research domain commonly referred to as 'eXplainable AI' (XAI) [15], aimed at unveiling the high complexity of the models obtained through machine learning methodologies as deep neural networks [16,17], ensemble methods [18,19], and support vector machines [20]. They also have vast application in various fields, including finance [21,22], medicine [23,24], and self-driving cars [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…However, complex models ironically require additional post-hoc methodologies to obtain explanations. These can be designed for specific types of models [18,29] or be model agnostic [27,30]. In terms of data type, explanations naturally vary across different variations of data.…”

Section: Introductionmentioning

confidence: 99%

A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data

Mazzine

Martens

2021

Applied Sciences

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Counterfactual explanations are viewed as an effective way to explain machine learning predictions. This interest is reflected by a relatively young literature with already dozens of algorithms aiming to generate such explanations. These algorithms are focused on finding how features can be modified to change the output classification. However, this rather general objective can be achieved in different ways, which brings about the need for a methodology to test and benchmark these algorithms. The contributions of this work are manifold: First, a large benchmarking study of 10 algorithmic approaches on 22 tabular datasets is performed, using nine relevant evaluation metrics; second, the introduction of a novel, first of its kind, framework to test counterfactual generation algorithms; third, a set of objective metrics to evaluate and compare counterfactual results; and, finally, insight from the benchmarking results that indicate which approaches obtain the best performance on what type of dataset. This benchmarking study and framework can help practitioners in determining which technique and building blocks most suit their context, and can help researchers in the design and evaluation of current and future counterfactual generation algorithms. Our findings show that, overall, there’s no single best algorithm to generate counterfactual explanations as the performance highly depends on properties related to the dataset, model, score, and factual point specificities.

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gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

Cited by 8 publications

References 24 publications

Conclusive Local Interpretation Rules for Random Forests

Conclusive Local Interpretation Rules for Random Forests

Detecting industrial discharges at an advanced water reuse facility using online instrumentation and supervised machine learning binary classification

A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data

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