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)

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

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

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

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Significantly Enriched Pathways. by Jiyong Zhang (2498740)

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

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

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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. …”

Significance of independent variables. by Halit Tutar (20678455)

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

Display of significance test results. by Qingchun Jiao (18388097)

“…Analysis of variance (ANOVA) indicates that, across multiple datasets constructed from fabrics with different elasticity grades, the model shows extremely significant differences (p < 0.001) in the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) at each prediction step. …”

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

Decrease of injuries. by Petr Fulin (8380320)

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

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

Overview of significant differences in experiment 1 and 2. by Gerrit Kollegger (10692496)

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

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. …”