ABSTRACT This study advances governance scholarship by applying robust machine learning techniques, bagging, random forest, boosting, SHapley Additive exPlanations (SHAP), and partial dependence plots (PDPs), to systematically explore how diverse board compositions (gender diversity, nonexecutive member diversity, independent board diversity) and the presence of board members with specific strategic skills (board‐specific skills percent) impact firms' environmental innovation outcomes. Using comprehensive governance data from the hospitality and tourism sector (Refinitiv, 2015–2024), results reveal strong predictive relationships, highlighting product responsibility as the most influential factor. The analysis further indicates that board‐specific skills and external diversity significantly amplify firms' environmental innovation, particularly when combined with proactive sustainability practices. SHAP and PDP analyses provide deeper insights into these nonlinear interactions, enriching theoretical perspectives drawn from Resource Dependency Theory, Upper Echelons Theory, and Stakeholder Theory. This study offers valuable strategic implications for industry practitioners aiming to leverage targeted governance structures to enhance sustainability‐driven innovation.

Abstract Background The use of incinerated bottom ash (IBA) as a sustainable construction material offers potential environmental benefits but introduces complex interactions with cement chemistry. Magnesium phosphate cement (MPC), known for its rapid hardening and superior bonding, can be optimized through the controlled incorporation of IBA. However, limited studies have addressed how the chemical components of IBA affect the compressive strength of MPC, particularly using data-driven approaches. Methods A database of 396 experimental samples was compiled from previous studies considering mix proportions, oxide compositions, and curing conditions. Four ensemble machine learning algorithms—Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Gradient Boosting Regressor (GBR), and Random Forest (RFR)—were employed to predict compressive strength. Model robustness was validated through 5-fold cross-validation. Feature interpretation was achieved using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to quantify individual and interactive effects of chemical and physical parameters. Results The XGB model achieved the highest predictive accuracy, with mean training and testing R2 values greater than 0.90 and 0.80, and the lowest mean absolute percentage error of 16.71%. SHAP analysis identified curing age as the most dominant factor, followed by FA/C, W/C, and MgO/PO4 ratios. IBA content and specific oxides such as Fe2O3 and Al2O3 contributed positively to strength within optimal ranges. PDP confirmed nonlinear dependencies, indicating a 26% reduction in strength as W/C increased from 0.1 to 0.6, while extended curing up to 28 days improved performance substantially. Conclusion The integration of SHAP and PDP provided a transparent interpretation of feature interactions in IBA-modified MPC. The developed XGB model demonstrated strong generalization and interpretability. The combined modeling approach offers a reliable predictive framework for optimizing IBA incorporation in sustainable binder systems and advancing eco-efficient material design.

PurposeTo develop predictive models for postoperative visual acuity—both near and distance, monocular and binocular—and a composite outcome metric, distance-corrected visual gain (DCVG), in patients undergoing presbyopic corneal refractive surgery.MethodsA retrospective analysis was performed on 914 eyes from 457 patients treated with PresbyMAX who completed 6 months of follow-up. Machine learning algorithms, including Random Forest regression and classification models, were applied to predict corrected distance visual acuity (CDVA) and distance-corrected near visual acuity (DCNVA), both measured using the same distance correction. Feature importance was evaluated using Shapley additive explanations (SHAP) and partial dependence plots (PDPs).ResultsBinocular models outperformed monocular in predictive accuracy, and CDVA was more predictable than DCNVA. A clinical trade-off was observed whereby improvements of up to three lines in DCNVA could be achieved without significant CDVA loss. The optimal planned addition ranged between +1.50 D and +2.25 D, balancing near and distance vision outcomes. Patients 45 to 56 years of age showed greater potential for near vision gain without compromising distance vision. The DCVG metric effectively quantified overall visual benefits, with only a minority of cases showing a net loss.ConclusionsPredictive modeling combined with the DCVG metric enables personalized surgical planning by identifying key factors influencing outcomes. This approach supports better management of the visual trade-offs inherent in presbyopic refractive surgery.Translational RelevanceMachine learning–based prediction models and the DCVG metric facilitate individualized clinical decision-making, improving patient counseling and optimizing the balance between near and distance vision in presbyopic treatment.

Generative physics-informed machine learning for modeling indoor air quality and its impact on student health and performance.

This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction. The RSF algorithm was applied to clinical prognostic data of 109 patients with malignant colonic obstruction who underwent self-expandable metallic stent (SEMS) procedures between September 2014 and October 2023. We combined the RSF variable importance and Least Absolute Shrinkage and Selection Operator (Lasso) regression to identify the final predictive variables. And the performance of the RSF model was compared with the Cox Proportional Hazards (CPH) model using both global and local explanation methods. The RSF model demonstrated superior predictive performance, with higher time-dependent AUCs and lower Brier scores compared to the CPH model across various time points. Significant predictors of stent patency identified by the RSF and Lasso models included Diabetes, CA199, Pre-Chemotherapy and Length of obstruction. The partial dependence plots highlighted CA199 and Length of obstruction as critical variables, with SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) analyses further revealing the dynamic, time-varying impact of these variables on individual patient outcomes. The RSF algorithm, supplemented with comprehensive feature importance analyses and advanced interpretability techniques, offers a robust and reliable framework for predicting stent patency risk in patients with malignant colonic obstruction.

In critically ill patients, a discrepancy frequently exists between percutaneous oxygen saturation (SpO₂) and arterial blood oxygen saturation (SaO₂), which can lead to potential hypoxemia being overlooked. The aim of this study was to explore the factors related to the discrepancy and to develop an easy-to-use prediction model that uses readily available bedside information to predict the discrepancy and suggest the need for arterial blood gas measurement. This is a prognostic study that used eICU Collaborative Research Database from 2014 to 2015 for model development and MIMIC-IV data from 2008 to 2019 for model validation. To predict the outcome of SpO₂ exceeding SaO₂ by 3% or more, non-invasive, readily available bedside information (patient demographics, vital signs, vasopressor use, ventilator use) was used to develop prediction models with three machine learning methods (decision tree, logistic regression, XGBoost). To make the model accessible, the model was deployed as a web-based application. Additionally, the contribution of each variable was explored using partial dependence plots and SHAP values. From 4,781 admission records in eICU data, a total of 19,804 paired SpO₂ and SaO₂ measurements were used. Among three machine learning models, the XGBoost model demonstrated the best predictive performance with an AUROC of 0.73 and a calibration slope of 0.90. In the validation cohort of MIMIC-IV paired dataset, the performance was AUROC of 0.56. An exploratory model-updating step followed by temporal validation raised performance to AUROC of 0.70 with a calibration slope of 0.85. In both datasets, worse vital signs were associated with the discrepancy (e.g., low blood pressure, low temperature) between SpO₂ and SaO₂. Using non-invasive bedside data, a machine learning model was developed to predict SpO₂-SaO₂ discrepancy and identified vital signs as key contributors. These findings underscore the awareness for hidden hypoxemia and provide the basis of further study to accurately evaluate the actual SaO₂.

Abstract To overcome the limitations of conventional single‐factor analysis, this study proposed a framework for investigating interaction effects of influencing factors on the resilient modulus (Mr) of stabilized aggregate base. First, cross‐validation was utilized to compare the predictive accuracy and generalization capability of gradient boosting (GB) and random forest (RF) in predicting the Mr. The grid search algorithm was used to optimize hyperparameters. After optimization, the coefficient of determination for GB reached 0.99 on the training set and 0.96 on the test set, while those for RF were 0.98 and 0.94, respectively. The results indicated that GB demonstrated higher predictive accuracy for the Mr. Finally, the importance analysis, univariate sensitivity analysis, and bivariate interaction sensitivity analysis of influencing factors were systematically conducted using partial dependence plots (PDP) and Shapley additive explanations (SHAP). The research results showed that the importance of influencing factors on the Mr decreases in the order of maximum dry density to optimum moisture content ratio, wet–dry cycles (WDC), deviator stress, confining pressure, and ratio of oxide compounds in the cementitious materials. The bivariate interaction sensitivity analysis of the WDC, deviator stress, confining pressure, and ratio of oxide compounds in the cementitious materials did not disrupt their single‐variable sensitivity relationships with the Mr. The variation of the WDC would destroy the single variable sensitivity relationship between the optimum moisture content ratio and Mr.

Enhancing interlayer bond strength remains a critical challenge in the extrusion-based 3D printing of cementitious materials. This study investigates the optimisation of interlayer bond strength in extrusion-based 3D-printed cementitious materials through a combined application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Using a concise yet comprehensive dataset, RSM provided interpretable main effects, curvature, and interactions, while the ANN captured non-linearities beyond quadratic forms. Comparative analysis revealed that the RSM model achieved higher predictive accuracy (R2=0.95) compared to the ANN model (R2=0.87). Desirability-based optimisation confirmed the critical importance of minimising casting delays to mitigate interlayer weaknesses, with RSM suggesting a water-to-cement (W/C) ratio of approximately 0.45 and a minimal time gap of less than 5 min, while ANN predicted slightly lower optimal W/C values but with reduced reliability due to the limited dataset. Sensitivity analysis using partial dependence plots (PDPs) further highlighted that ordinary Portland cement (OPC) content and W/C ratio are the dominant factors, contributing approximately 2.0 and 1.8 MPa respectively to the variation in predicted bond strength, followed by superplasticiser dosage and silica content. Variables such as water content, viscosity-modifying agent, and time gap exhibited moderate influence, while sand and fibre content had marginal effects within the tested ranges. These results demonstrate that RSM provides robust predictive performance and interpretable optimisation guidance, while ANN offers flexible non-linear modelling but requires larger datasets to achieve stable generalisation. Integrating both methods offers a complementary pathway to advance mix design and process control strategies in 3D concrete printing.

In critically ill patients, a discrepancy frequently exists between percutaneous oxygen saturation (SpO₂) and arterial blood oxygen saturation (SaO₂), which can lead to potential hypoxemia being overlooked. The aim of this study was to explore the factors related to the discrepancy and to develop an easy-to-use prediction model that uses readily available bedside information to predict the discrepancy and suggest the need for arterial blood gas measurement. This is a prognostic study that used eICU Collaborative Research Database from 2014 to 2015 for model development and MIMIC-IV data from 2008 to 2019 for model validation. To predict the outcome of SpO₂ exceeding SaO₂ by 3% or more, non-invasive, readily available bedside information (patient demographics, vital signs, vasopressor use, ventilator use) was used to develop prediction models with three machine learning methods (decision tree, logistic regression, XGBoost). To make the model accessible, the model was deployed as a web-based application. Additionally, the contribution of each variable was explored using partial dependence plots and SHAP values. From 4,781 admission records in eICU data, a total of 19,804 paired SpO₂ and SaO₂ measurements were used. Among three machine learning models, the XGBoost model demonstrated the best predictive performance with an AUROC of 0.73 and a calibration slope of 0.90. In the validation cohort of MIMIC-IV paired dataset, the performance was AUROC of 0.56. An exploratory model-updating step followed by temporal validation raised performance to AUROC of 0.70 with a calibration slope of 0.85. In both datasets, worse vital signs were associated with the discrepancy (e.g., low blood pressure, low temperature) between SpO₂ and SaO₂. Using non-invasive bedside data, a machine learning model was developed to predict SpO₂–SaO₂ discrepancy and identified vital signs as key contributors. These findings underscore the awareness for hidden hypoxemia and provide the basis of further study to accurately evaluate the actual SaO₂.

Accurate estimation of the ultimate bearing capacity (UBC) of shallow foundations is critical for safe and economical geotechnical design. Traditional approaches depend heavily on extensive and costly field and laboratory investigations, while numerical simulations, though effective, are computationally intensive and time-consuming. To address these limitations, this study investigates the application of machine learning (ML) models for efficient and reliable prediction of the ultimate bearing capacity of shallow foundations. Although numerous studies have explored individual ML techniques for this purpose, a comprehensive and consistent comparison of widely used models under identical conditions remains limited. This research evaluates six ML algorithms; k-Nearest Neighbors (kNN), Artificial Neural Network (NN), Random Forest (RF), Extreme Gradient Boosting (xGBoost), Adaptive Boosting (AdaBoost), and Stochastic Gradient Descent (SGD), using a dataset of 169 experimental results collected from literature. The input features include foundation width (B), depth (D), length-to-width ratio (L/B), soil unit weight (γ), and angle of internal friction (φ). Model performance was assessed using multiple evaluation metrics: coefficient of determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and objective function (OBJ). To enhance model interpretability, SHapley Additive Explanations (SHAP) and Partial Dependence Plots (PDPs) were employed to analyze feature importance and input-output relationships, highlighting the influence of both soil properties and foundation geometry on predicted bearing capacity. Among the evaluated models, AdaBoost demonstrated the best overall performance, achieving R² values of 0.939 and 0.881 on the training and testing sets, respectively. Based on the cumulative ranking of the models across all evaluation metrics, the models were ranked in the following order of performance: AdaBoost > kNN > RF > xGBoost > NN > SGD. While the results are promising, a key limitation is the use of single-layer soil data, which restricts applicability to more complex, multilayered soil profiles. Future studies should incorporate multilayer datasets and account for spatial variability to enhance the generalizability and robustness of predictive models.

With the acceleration of urbanization, the coupling relationship between the built environment and urban safety hazards has become increasingly prominent. Irrational spatial structures and resource allocations may aggravate safety hazards and negatively affect residents’ quality of life, thus requiring urgent scientific evaluation and optimization. However, existing studies mostly focus on linear correlation analysis, which makes it difficult to reveal the complex nonlinear mechanisms among multidimensional environmental factors. Taking Cracow (Kraków), Poland as the study area, this research utilizes multi-source spatial data to quantify environmental features such as transportation, socioeconomic conditions, visual landscapes, and public services, in order to uncover their role in the formation of safety hazards. An XGBoost-based safety hazard prediction model is constructed, and SHAP interpretability analysis, together with two-dimensional partial dependence plots (2D PDPs), are introduced to systematically explore the synergistic gains, marginal effects, and resource allocation thresholds of key variables. The results indicate that variables such as average housing price, distance to the nearest police station, and average population density contribute significantly to hazard prediction, and that certain combinations of variables exhibit strong synergistic effects in reducing hazards within medium-range intervals. The study concludes that integrating machine learning with interpretability analysis can not only effectively identify the spatial features associated with high levels of safety hazards, but also provide quantifiable and actionable optimization pathways for urban planning and safety hazard governance. This research further underscores the role of managing urban safety hazards as a key pillar in the sustainable development of cities by linking safety hazard modeling with spatial governance strategies that promote inclusive, resilient, and livable urban environments.

This study employs the uniform design (UD) method to establish a sample database and utilises a machine-learning-based partial dependence plot (PDP) approach to develop a multi-parameter seismic fragility analysis framework for horizontally curved bridge portfolios. Six machine learning (ML) models are compared to identify the optimal seismic fragility prediction model and assess the importance of parameters through permutation importance (PI). Additionally, the PDP is applied to generate seismic fragility curves for the curved bridge portfolios, effectively avoiding the need for high-dimensional mixed integrals. Furthermore, the PDP technique is used to explore the relationship between critical structural attributes and fragility under specified seismic intensity levels. The results indicate that the ML model exhibits favourable fidelity and robustness, with an area under the curve (AUC) exceeding 0.9. The most influential structural attributes affecting the fragility of horizontally curved bridges include boundary type (BT), support design configuration (SPD), column height (H), single-span length (L), and central angle (α). The ductility seismic system and the seismic isolation system for horizontally curved bridges can be preliminarily distinguished based on parameters α, L, and H.

IntroductionFrequent droughts and climate fluctuations pose significant challenges to stabilizing and increasing the yields of drought-tolerant crops like sorghum. Accurate, detailed, and spatially explicit yield predictions are essential for precision irrigation, variable fertilization, and food security assessment.MethodsThis study was conducted in the Lifang dryland experimental area in Jinzhong, Shanxi Province, using a sorghum planting experiment. Multispectral imagery and meteorological data were collected simultaneously using a DJI Mavic 3M UAV during key growth stages (seedling emergence, jointing, flowering, and maturity). A “spectral-meteorological-spatial” three-dimensional prediction framework was developed using eight machine learning algorithms. SHAP values and Partial Dependency Plots were used to assess variable importance.ResultsEnsemble learning algorithms performed best, with the Gradient Boosting model achieving an R2 of 0.9491 and Random Forest reaching 0.9070. SHAP analysis revealed that DVI and NDGI were the most important predictors. The jointing stage contributed most to prediction accuracy (R2 = 0.9454), followed by maturity (R² = 0.9215) and flowering (R2 = 0.9075). Yield spatial distribution ranged from 4,291 to 4,965 kg haR-1, with a global Moran’s I index of 0.5552 indicating moderate positive spatial autocorrelation.DiscussionIntegrating UAV multispectral data with machine learning methods enables efficient sorghum yield prediction, with the jointing stage identified as the optimal monitoring period. This study provides crucial technical support for precision planting and efficient sorghum management in arid regions.

Abstract The rapid development of the Internet of Things (IoT) has transformed intelligent transportation systems. However, traffic accidents remain a significant challenge, and it is imperative to develop effective strategies to reduce their frequency and severity. This research introduces a hybrid model that integrates Adaptive Bayesian Optimization (ABO) with Random Forest (RF) to enhance the prediction of traffic accident severity. By leveraging ABO to optimize RF parameters, the model achieves superior accuracy compared to conventional approaches while maintaining interpretability through relative importance and partial dependence plots. Furthermore, utilizing real-time vehicle data, the system implements context-based vehicle accident detection within vehicular ad hoc networks (VANETs). The integration of Enhanced Local Adaptive Machine Learning (ELAML), RF, and ABO-RF demonstrates robust performance in collision detection, outperforming models such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN), with a detection accuracy of 90%. Compared to existing models, the proposed ABO-RF achieved the highest accuracy (90%), perfect recall (100%), and the best F1 score (94.74%), with lower error rate (10%), FAR (6.00%), MDR (25.00%), and detection latency (25.24%) than conventional methods such as MLP, ResNet-50, AdaBoost and XGBoost. These findings offer valuable insights into improving road safety and mitigating accident severity in the Internet of Vehicles (IoV)-enabled intelligent transportation systems.

With increasing demand for capacity improvement, future highway construction, and the need for newer infrastructure, the United States is likely to experience more, longer duration, and longer stretches of work zones along its roadways. Work zones are often characterized by lower speeds, changes in traffic patterns, and sometimes narrower lanes, leading to safety compromises of workers, motorists, and other road users. As higher speed limits usually characterize freeways, the significant speed reductions along work zones create substantial shifts for drivers. The abrupt changes in speed and traffic patterns influence the occurrence of crashes as well as injury severity. This study used 2018 to 2022 Florida work zone data in conjunction with freeway crashes to determine the factors influencing the injury severity of crashes occurring along its freeway work zones. Six models, including the K-nearest neighbor, support vector machine, random forest, extreme gradient boosting, multinomial logistic regression, and ordinal logistic regression, were compared for prediction performance using various metrics. The random forest model was observed to be superior in its classification capacity and therefore used for the data analysis. Variable importance and partial dependency plots were used to interpret the model and understand the influence of roadway, environmental, temporal, and human factors associated with work zone crash injury severity. The findings from this study will be helpful to traffic engineers and other responsible agencies in freeway work zone planning to reduce crash injuries and improve roadway safety.

Poverty remains a multidimensional challenge in Central Java, necessitating robust data-driven approaches to identify its socioeconomic determinants. This study applied six machine learning models, specifically Extreme Gradient Boosting (XGBoost), Random Forest, CatBoost, LightGBM, Elastic Net Regression, and a Stacking ensemble using district-level data from Statistics Indonesia covering demographics, education, labor, infrastructure, and household welfare. Model evaluation combined an 80:20 hold-out split, 10-fold cross-validation, and noise perturbation tests. Results show that XGBoost achieved the best individual performance (MAE = 2,180.01; RMSE = 3,512.07; R² = 0.931), while the Stacking ensemble surpassed all single learners (MAE = 2,640.99; RMSE = 3,202.79; R² = 0.942). Interpretability was ensured through SHAP (Shapley Additive Explanations), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE), consistently identifying Number of Households, Per Capita Expenditure, and Uninhabitable Houses as the most influential predictors. Counterfactual simulations indicated that increasing per capita expenditure by 10% could reduce the poverty index by 9.9%, while reducing household size by 10% lowered it by 11.3%. Robustness checks revealed Brebes as an influential district shaping model stability. Overall, the findings demonstrate that boosting and stacking ensembles, when combined with explainable AI tools, not only enhance predictive accuracy but also provide transparent, policy-relevant evidence to strengthen poverty alleviation programs in Central Java. This study contributes both methodological advances in explainable machine learning and practical insights for targeted poverty reduction strategies.

ABSTRACT This study utilized gene expression programming (GEP) and multi-expression programming (MEP) machine learning techniques to predict the autogenous shrinkage (AS) of concrete incorporating superabsorbent polymers (SAPs) and cementitious supplements, eliminating the need for labor-intensive and costly experimental procedures. Both the MEP and GEP models demonstrated strong performance in predicting the shrinkage and swelling of concrete containing SAPs and SCMs, with GEP emerging as the most effective. GEP achieved an impressive R2 of 0.98, underscoring its enhanced forecasting capability over MEP, which recorded an R2 of 0.94. The SHapley Additive exPlanations along with partial dependence plots underscored the notable impact of several parameters on the AS of concrete. Notably, the aggregate-to-cement ratio (Agg/C) and SAP proportion were identified as the most influential factors, demonstrating a significant decrease in shrinkage with their increase. In contrast, both the duration of the observation period and SAP size contributed to an increase in shrinkage. The partial dependence plots revealed that shrinkage decreases sharply with an increase in both the Agg/C ratio and SAP content, while it increases significantly as the SAP size grows. A graphical user interface has been developed, enabling users to input the necessary parameters and obtain predictions for AS in high-performance concrete without conducting experiments.

This paper used machine learning in assessing the mitigates of primary school dropout risk in Narok West Sub- County, Kenya. Although the use of bursaries, school feeding and community sensitization has been long held, current interventions are reactive meaning that they deal with dropouts once they stop attending school. The predictive modeling to predict dropout and inform preventative action developed using structured field survey (n= 1,000) with Monte Carlo simulation extending to 10,000 records. Three classifiers, namely, Random Forest, XGBoost, and Support Vector Machine were trained on an 80/20 split with five-fold cross-validation and measured in terms of accuracy, precision, recall, F1-score, and ROC-AUC. XGBoost has obtained the best results (AUC = 0.804; F1 = 0.771), which makes it the model that has been validated. The findings of Chapter Four have revealed that financial considerations prevailed in risk dynamics: bursary receipt and bursary amount had a significant negative effect on dropout whereas monthly fees donations and traveling a long distance contributed to the level of dropout. The welfare programs like school feeding, meals per day and community participation were identified as the important protective factors. To make sure the results could be interpreted, explainable AI methods such as permutation importance, SHAP values, and partial dependence plots revealed both the importance and direction of the influence of every factor, not just in prediction but actionable insights. Its results show that financial strain mediated by structural and social supports is the leading cause of dropout, and that predictive analytics can offer policy-makers evidence-based drops to intervene. The integration of such models into the education planning provides a proactive channel of maintaining learner retention and enhancing equity in the rural schooling. The evaluation work helps construct an education system that stands resilient along with technology development and social fairness in Narok West Sub County.

Partial dependence plots (PDPs) and individual conditional expectation (ICE) plots are visualizations used for explaining the behavior of machine learning (ML) models trained on tabular datasets. They show how the values of a feature or pair of features impact a model's predictions. However, in models with a large number of features, it is impractical for an ML practitioner to analyze all possible plots. To address this, we present new techniques for ranking and filtering PDP and ICE plots and build upon existing strategies for clustering the lines in ICE plots. Together, these techniques aim to help ML practitioners efficiently explore PDP and ICE plots and identify interesting model behavior. We integrate these techniques into PDPilot, a visual analytics tool that runs in Jupyter notebooks. We use PDPilot to study how 7 ML practitioners utilize the ranking, filtering, and clustering techniques to analyze an ML model.

Understanding the spatial patterns of major geo-hydrological disasters in Italy using the CatBoost algorithm.