By changing the number, frequency, and ORs of simulated variants we created baseline models with an AUC ranging between 0.50 and 0.95.
The mean absolute error (MAE) of the map query model declines 67% below that of the baseline model, with a value of 13.2 million RMB (the mean of daily box office is 118 million, with a standard deviation of 75.8 million), as shown in Figure 8B.
Overall, the best individual secondary data source, the mean fixed rule and the weighted trained rule outperformed the baseline model with a p-value of 0.0313, 0.0313 and 0.0313, respectively.
For the ANCOVA we constructed baseline models with the measures of brain structure and cognitive ability as dependent (i.e. outcome) variables and neck muscle CSA as an independent variable, adjusting for intracranial area (ICA) and age.
We design a nine-layer CNN for HCCR consisting of 3755 classes, and devise an algorithm that can reduce the network's computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0.21% drop in accuracy.
In this section, we present an adjusted baseline model with no IDR in effect, but with a loss offset restriction on corporate level.
Final multivariate model as shown in Table 1, 95% confidence intervals, p-value for likelihood ratio tests compared to baseline model (model 1), Model 4 is the baseline model with the addition of an interaction term; the p value is for the interaction term.
Tests of model invariance compare fit statistics of a baseline model with subsequent models with additional constraints.
The CFI is an incremental fit index and tests the proportionate improvement in fit by comparing the target model to a baseline model with no correlations among observed variables.
The addition of UCT alone, SSS alone and both UCT and SSS to a baseline model with clinical risk factors consisting of LVEF, time since CABG and EuroSCORE II significantly improved both the NRI and IDI.
