The distribution of feature impact on model predictions of the treatment (positive SHAP values) and the control group (negative SHAP values) as a SHAP bee-swarm plot. by Kamil Książek (22177742)

SHAP dependence plots. by Yanxuan Wu (9623598)

Sample Outcomes with SHAP Values. by Badar Almarri (11337192)

“…<div><p>Alzheimer’s disease (AD) poses significant challenges to healthcare systems across the globe. …”

The number of gauging cross-sections where a statistically significant decreasing trend was identified. by Krzysztof Kochanek (835532)

SHAP analysis of a sample. by Yun Zuo (8541492)

Mean decrease Gini score by Tweet and user characteristics. by Gray Babbs (16648894)

Decrease of the mean number of natural teeth and POPs with age. by Sul-Hee Kim (11873853)

Global SHAP summary plot. by Yahui Wang (1305345)

Subjects:

ADNI neuropsychological test clinical data summary showing average values with standard deviation. Statistical significance (95% significance level) was tested using analysis of Kr... by Sherlyn Jemimah (10328552)

SHAP value feature importance graph. by Wangyouchen Zhang (21681025)

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

“…<p>(A) The SHAP summary plot of the GBM model. (B) Significance analysis of feature importance ranking via SHAP, based on the mean value. …”

Feature importance based on SHAP values, depicted as a bar plot. by Probir Kumar Ghosh (6947282)

The influence of the controlled pH conditions at the sample preparation step on the obtained signals (panel A: extraction using a mixture of MeOH and EtOH; panel B: extraction usin... by Ewa Paszkowska (21246702)

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Flow diagram illustrating the sequence of procedures executed during one time step defined as tick, each of which represents one week. by Alexandra M. Campbell (3284094)

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SHAP bar plot. by Meng Cao (105914)

“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

SHAP summary plot. by Meng Cao (105914)

“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”

SHAP Feature Importance: Mean Absolute SHAP Values in the XGBoost Final Model for Predicting MASLD. by Yong Zhang (5893)

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SHAP analysis. by Hexin Li (8641479)

SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

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The slope for all significant factors on the number of steps before a fall across all three speeds. by Amy Mitchell (3208905)