The GmNN1/FT2a-GmNFYA-C-GmENOD40 module regulates soybean nodulation. by Xinxin Li (281237)

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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. …”

Significant decrease of the viability of A375 melanoma cells after CD treatment. by Lena Berning (7430117)

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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Concentration of RebA and Steviol showed increased significantly, whereas stevioside showed significant decrease in all the transgenic lines. by Nazima Nasrullah (6874496)

“…<p>Concentration of RebA and Steviol showed increased significantly, whereas stevioside showed significant decrease in all the transgenic lines.…”

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

“…<p>The number of gauging cross-sections where a statistically significant decreasing trend was identified.…”

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)

A proposed working model of how the GmNN1/FT2a-GmNYFA-C-GmENOD40 module regulates nodulation in soybean. by Xinxin Li (281237)

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Exercise is associated with a decrease in BMI. by Stephanie Y. Yang (8384520)

“…<p>There was a significant association between a greater number of exercise sessions attended and decreased BMI over the 6-month intervention period.…”

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

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