Showing 1 - 20 results of 7,106 for search '(( using ((shap decrease) OR (we decrease)) ) OR ( a ((greatest decrease) OR (largest decrease)) ))', query time: 0.48s Refine Results
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    Differentially expressed genes (DEGs)<sup>a</sup> showing the greatest fold changes from each potato tissue: 10 with greatest increase in expression and 10 with greatest decrease in expression. by Margaret A. Carpenter (6104180)

    Published 2025
    “…<p>Differentially expressed genes (DEGs)<sup>a</sup> showing the greatest fold changes from each potato tissue: 10 with greatest increase in expression and 10 with greatest decrease in expression.…”
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    SHAP waterfall plot. by Wentao Yang (205781)

    Published 2025
    “…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”
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    SHAP decision plot. by Wentao Yang (205781)

    Published 2025
    “…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”
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    SHAP dependence plots. by Wentao Yang (205781)

    Published 2025
    “…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”
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    SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

    Published 2025
    “…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”
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