SHAP-based feature importance ranking. by Jinrong Qi (22170340)

“…The results show that the combined application of feature selection, data augmentation, and SHAP-based optimization significantly improves the model’s prediction performance. …”

Sizes and similarity of the individual Gini-based and SHAP-based SPARTA selections. by Baptiste Ruiz (20285217)

SHAP-based interpretation of influencing process parameters on compressive strength. by R. S. Jayaram (22139155)

Feature importance of risk factors based on SHAP values, (a) Mean absolute SHAP values (b) Local explanation summary. by Shamsuz Zoha (22339469)

Subjects:

SHAP Values Distribution of Clinical Features. by Luyu Chai (12098204)

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SHAP value summary graph. by Wangyouchen Zhang (21681025)

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Importance ranking of the affecting factors based on RF-SHAP model. by Feifei Cheng (8985345)

SHAP summary plot for the XGBoost-based suicide prediction model. by Fatemeh Gholi Zadeh Kharrat (9073335)

SHAP interprets the mode. by Qiudie Liu (22655907)

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. …”

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

Global SHAP summary plot. by Yahui Wang (1305345)

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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)

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. …”