The phrase "auc model" is correct and usable in written English, assuming "auc" refers to "Area Under the Curve" in a statistical context.
You can use it when discussing performance metrics for classification models, particularly in machine learning and data analysis.
Example: "The auc model indicates that our classifier has a high level of accuracy in distinguishing between the two classes."
Alternatives: "AUC metric" or "Area Under Curve model".
In order to evaluate the convergence speed of the AUC model, we determined the FPR at each boosting stage.
This means that conventional models require more iterations and that the AUC model cascade can converge much faster.
The results show that, in the AUC model, the FPR f j at each cascade stage is adaptive among the different stages, ranging from 0.04486 to 0.26837, and is much smaller than the conventional model FPR of 0.5.
An area-under-the-curve (AUC) model (or partitioned survival model) was used, consisting of three health states, ie, progression-free survival, progression, and death (see Figure 1).
Ten iterations of the model were run with random seed to derive mean and standard deviation (SD) of AUC model scores.
By analysis of AUC, Model 1 showed enhanced discrimination compared to the SOFA and ECMOnet score (P = 0.03 and P = 0.01, respectively, Table 2, Figure 1).
Dose, AUC, model-predicted baseline levels of the biomarkers and tumor size, and the relative change over time in tumor size and biomarkers were evaluated as predictors of overall survival.
The four assessed here are the two models with the highest AUC (models 2 and 3) and two models with the lowest AUC (models 1 and 4).
The FPC1 scores and the AUC (Models 1 and 4, respectively) yielded nearly identical results, thus the results for AUC are not shown.
Differences in the predictive power of fractalkine and soluble E-selectin was studied by comparing the ROC AUC models by means of DeLong's test.
The Mann-Whitney-U Test was used for the univariate analysis and the AUC-Model for discrimination of the scores.
