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In order to evaluate the information added by our proposed features as compared to the previously widely used Adamic-Adar and overlap metrics, we compare our prediction results thus far with a simplified model using the Adamic-Adar and overlap features alone, while using the same predictive framework, and compare the change in average AUC scores between cities.
Likewise, Figure 3b shows similar performance in ACs 2 and 3 for a model trained and tested on AGL data and a model using the same predictive features trained and tested on AFX data.
Corresponding results for samples in each subclass using different classification algorithms are illustrated in Figure 3(c d) and Figure S3. Figure 3a shows very similar performance in AC 2 and AC 3 for a model trained and tested on AFX data and a model using the same predictive features trained and tested on AGL data.
For every sample, a regression model was run using the same predictive variables.
The second DFA was conducted using the same predictive traits as the first, but it was conducted only on F2 samples (n = 8925) to determine whether environmental or year effects contributed to the prediction.
When we built a logistic regression model using the same predictive variables and using PCA3 as a continuous value we obtain an AUC of 0.769 (data not shown), very similar to the application of Hansen nomogram in our series (Table 3).
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In addition, the performance of DBERM on fitting each rat's IRT distribution was assessed using the same posterior predictive check.
One way to overcome this complication is to estimate predictive accuracy using the same model used to define predictive accuracy, provided the model is close to the model for some genomic prediction method.
Using the same framework of Model Predictive Control (MPC), but without the cost of online optimization that usually occurs in the MPC algorithms, this paper shows the design and implementation procedures of repetitive control of multi-input and multi-output systems with anti-windup mechanisms.
The comparison was performed in the same way as described by Brohee & van Helden [ 29], using the same measures of sensitivity, Positive Predictive Value (PPV) and accuracy.
Using the same tester in Experiment 1, predictive ability estimates obtained under validation schemes V1 and V2 were similar using four environments for the training set and two common or different environments for the validation set.
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