SHAP summary plot. by Meng Cao (105914)

Main study: SHAP summary plots for XGB model. by Rajeev Bopche (20208606)

Case study: SHAP summary plots for XGB model. by Rajeev Bopche (20208606)

SHAP summary plot of feature importance ranking of GA-XGBoost model. by Wenguang Li (6528113)

Pearson correlation coefficient matrix plot. by Wenguang Li (6528113)

Machine learning interpretability visualization. by Xie Qiu (13243770)

Table1_Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.docx by Ren Zhou (644036)

“…Background<p>Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort.…”

Results of segmentation accuracy. by Atsushi Hamabe (12890022)

SHAP analysis of the GBM model. by Lang Gao (12963876)

Supplementary file 1_Machine learning-based predictive model for acute pancreatitis-associated lung injury: a retrospective analysis.docx by Zhaohui Du (414105)

“…The global interpretability of the XGBoost and RF models, along with these six features, is shown in the SHAP summary plot. These two models were selected as the optimal models for the development of an online calculator for clinical applications and risk stratification.…”

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 by Andrew Rogers (17623239)

“…</p><h2>3. VRE Siting Algorithm and Optimization</h2><p><br></p><p dir="ltr">The VRE siting model uses a cost-minimization optimization approach to select the most cost-efficient project sites to meet a projected energy mix target.…”