Raw data, predicted values, model significance levels, and standard errors. by Luis Diaz-Garcia (2968218)

Cinacalcet administered early in the inactive phase markedly decrease parathyroid Ki-67 index. by Søren Egstrand (10906087)

Prediction error according to prediction interval. by Joohwang Lee (19493041)

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Significantly Enriched Pathways. by Jiyong Zhang (2498740)

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Model prediction errors. by Julia H. Chen (16745256)

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Expected proportions of false discoveries of the significance. by Wojciech Charemza (7629338)

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The level of miRNA 221 in the myocardium of MIRI mice was decreased. by Hongyu Li (1332669)

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Average prediction errors of DPFTransformer. by Xiaofeng Wu (288608)

Incidence of illusory percepts was significantly different across all tested conditions. by Robert Staadt (8796263)

Model prediction error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Decrease of injuries. by Petr Fulin (8380320)

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Predicted infected people with error bars. by Zhichao Ma (2458558)

Prediction error by algorithmic players. by Niccolo Pescetelli (7000043)

Multi-step prediction error. by Yubo Zhao (3873457)

Prediction errors according to glaucoma severity. by Joohwang Lee (19493041)

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Prediction error results for each model. by Haneul Eom (11426722)

Predicted risk of self-harm for each of the 5 significantly predictive PSs and their combined predicted risk. by Kai Xiang Lim (8917556)

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Model prediction error trend chart. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Model prediction error analysis index. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”