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)

Subjects:

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)

Baseline patient characteristics. by Oscar F. C. van den Bosch (22184246)

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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)

Variability and correlations decrease during task states in human fMRI data. by Takuya Ito (57473)

“…<b>h)</b> We found that the average correlation for each brain region, decreased for each brain region during task state. …”

The ten most significant features of the Random Forest model based on classification accuracy. by Nguyen Tat Thanh (10296398)

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SHAP analysis in XGBoost, Mexico 2022. by Oliver Mendoza-Cano (9148646)

Subjects: