Approximating XGBoost with an interpretable decision tree

Sagi, Omer; Rokach, Lior

doi:10.1016/j.ins.2021.05.055

Cited by 236 publications

(79 citation statements)

References 22 publications

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“…These capabilities make DT widely employed for different applications that require a comprehension of both the model construction and its prediction. Although decision tree-based prediction models are highly interpretable, these intelligent decision-making models have limited prediction performance due to the nearsightedness characteristic of their induction models [ 89 , 90 ]. When complex interactions exist among input features, DT models usually fail to capture these, leading to essential biases.…”

Section: Proposed Xai-based Model For Covid-19 Diagnosismentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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Section: Proposed Xai-based Model For Covid-19 Diagnosismentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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“…The results were presented in the Supplementary Table 2 ( Supplementary File 1 ). Finally, we set the sampling time-window to 24 h and the sliding time-step to 8 h. Then, we developed several state-of-the-art models that are widely used as follows: (1) Classic machine learning models: Logistic regression (LR) ( 24 ) and support vector machines (SVM) ( 25 ), which are the most commonly used algorithms in existing research; (2) Enhanced machine learning models: gradient boosting machine (LightGBM) ( 26 ) and XGBoost ( 27 ), which are widely regarded as the best algorithm for data prediction and are adopted by many competition winning models in the field of machine learning; (3) Classic deep learning models: RNN ( 21 ) and long short-term memory network (LSTM) ( 28 ), which are the most commonly chosen deep learning models in time-series data, which have shown excellent performance in several time series studies; (4) Improved deep learning models: RNN-Decay and ODE-RNN. The detailed method was described in Supplementary File 1 .…”

Section: Methodsmentioning

confidence: 99%

“…The results were presented in the Supplementary Table 2 (Supplementary File 1). Finally, we set the sampling timewindow to 24 h and the sliding time-step to 8 h. Then, we developed several state-of-the-art models that are widely used as follows: (1) Classic machine learning models: Logistic regression (LR) (24) and support vector machines (SVM) (25), which are the most commonly used algorithms in existing research; (2) Enhanced machine learning models: gradient boosting machine (LightGBM) (26) and XGBoost (27), which are widely regarded as the best algorithm for data prediction and are adopted by many competition winning models in the field of machine learning;…”

Section: Model Developmentmentioning

confidence: 99%

An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

Cui

Hua

et al. 2021

Front. Med.

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Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

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“…The Gradient-Boosting Decision Tree (GBDT) is a machine learning technique for regression, classification, and other tasks, using a decision tree flowchart approach combined with the boosting ensemble technique. The GBDT improves the capacity of the decision tree by reducing the residuals generated during the training procedure [22,23]. It has been widely applied in social science research [24][25][26][27][28] and gradually introduced into the field of natural science [1][2][3][4][5][6][7][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model

et al. 2021

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Remote sensing for the monitoring of chlorophyll-a (Chl-a) is essential to compensate for the shortcomings of traditional water quality monitoring, strengthen red tide disaster monitoring and early warnings, and reduce marine environmental risks. In this study, a machine learning approach called the Gradient-Boosting Decision Tree (GBDT) was employed to develop an algorithm for estimating the Chl-a concentrations of the coastal waters of the Beibu Gulf in Guangxi, using Landsat 8 OLI image data as the image source in combination with field measurements of Chl-a concentrations. The GBDT model with B4, B3 + B4, B3, B1 − B4, B2 + B4, B1 + B4, and B2 − B4 as input features exhibited higher accuracy (MAE = 0.998 μg/L, MAPE = 19.413%, and RMSE = 1.626 μg/L) compared with different physics models, providing a new method for remote sensing inversion of water quality parameters. The GBDT model was used to study the spatial distribution and temporal variation of Chl-a concentrations in the coastal sea surface of the Beibu Gulf of Guangxi from 2013 to 2020. The results showed a spatial distribution with high concentrations in nearshore waters and low concentrations in offshore waters. The Chl-a concentration exhibited seasonal changes (concentration in summer > autumn > spring ≈ winter).

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Approximating XGBoost with an interpretable decision tree

Cited by 236 publications

References 22 publications

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model

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