Sentence examples for achieve predictive accuracy from inspiring English sources

As shown in the previous section, two equivalent models (based on two Markov boundaries) developed in NARAC training set achieve predictive accuracy 0.81 AUC (95% confidence interval: [0.78; 0.84] AUC) in NARAC testing set.

For predictive classification, only a subset of discriminatory genes is used to avoid overfitting, where a classifier is known 'too well' to fit even irreproducible 'noisy' training patterns and, thus, to achieve predictive accuracy that generalises well to unseen/test data.

Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.

Notably, Arshadi et al. achieves predictive accuracy greater or equal to 0.77 AUC only for models based on at least 5,000-10,000 SNPs which is four orders of magnitude larger than the number of SNPs in our causal graph-based models (with five SNPs only).

When the total material was fitted into the model, the following results were achieved: prediction accuracy 77%, sensitivity 0.30, specificity 0.95, positive predictive value 0.68, negative predictive value 0.78, and area under the ROC 0.69.

Similarly, the GEMS-SVM classifier with the 6 predictive genes achieved prediction accuracies of 88.9%, 87.5% and 89.8% respectively for three acetaminophen data sets from NCT008, NTP and NCT informatics challenge.

However, it is hard to achieve predictive, accurate load sharing and voltage regulation by using the droop control without communication, and moreover, both line impedances and output impedances of DGs will affect the accuracy of load sharing [9, 10].

The classifier for kidney-selective gene prediction achieved predictive performance with overall accuracy at 93.62% with MCC = 0.4648 and ROC AUC = 0.9300.

Physical models can achieve high predictive accuracy if appropriately built.

The results are very simple rules that can achieve high predictive accuracy.

It is often possible to achieve better predictive accuracy using a two-stage approach, where we first use a preprocessing feature selection method and then apply a decision tree building algorithm to the features selected in the first stage.