"greater predictive performance" is a perfectly correct and usable phrase in written English.
You could use this phrase, for example, to describe a scientific report that concludes that a particular model has improved accuracy when predicting an outcome. For example: "This study found that the newly developed model had greater predictive performance than the previous model."
In addition, the use of mechanistic elements in models provides for greater predictive performance to settings outside of which the model was created.
The rs2867461 + rs2293347 model has greater predictive performance than clinical parameters, each single SNP (rs2867461/rs2293347), or environmental factors, and the rs2867461 + rs2293347 model had a sensitivity of 69.0 % and specificity of 100.0 %.
Our experiments detail the performance of ISOLATE under a wide range of realistic experimental conditions for synthetic and digitized clinical microarray data, showing that solving all three tasks simultaneously leads to greater predictive performance than solving them individually.
The results show that the ANN based model exhibits a great predictive performance compared with MLR model according to correlation coefficient and the root-mean-squared error.
Utilizing 465 data samples (e.g., where 11 ionic liquids are included), the new strategy presented in this study demonstrates great predictive performance so that the coefficient of determination (R2) and mean squared error (MSE) are determined to 0.997594 and 6.6507E−05, respectively.
ESRD and death were the outcomes for which the comorbidity measures resulted in the greatest improvement in predictive performance.
C-statistics were lower and Brier scores were higher in all models in the age-restricted cohort compared to their equivalent models in full cohort, but the same comorbidity measures were identified as having the greatest improvement in predictive performance.
23 This is equal to the area under the receiver operator curve in a logistic regression model and lies between 0.5 and 1; values greater than 0.7 indicate good predictive performance and values greater than 0.8 indicate excellent predictive performance.
Morbidity inferred from diagnoses and drugs dramatically increased the predictive performance, the greatest effect found for conditions linked to an ACSC.
The inability of single SNP to effectively discriminate between eventual phenotype has led to the suggestion that utilizing a panel of SNPs, each associated with a physical capacity deemed contributory to elite performance, may provide greater predictive ability.
A value between 0.7 and 0.8 is considered to demonstrate acceptable predictive performance, while a value greater than 0.8 demonstrates excellent predictive performance.
