In statistical prediction, the independent dataset test, subsampling or K-fold crossover test and jackknife test are the three cross-validation methods often used to check a predictor for its accuracy [ 92].
A predictor for the accuracy of an imputed site generated by minimac is the r ^ 2, a quantity calculated by comparing the variance of observed genotype scores with the variance of expected genotype scores to estimate the squared correlation at a marker between the true allele counts and the estimated allele counts (Li et al. 2010).
In summary, we advise against the use of double-testing new predictors for predictive accuracy in the presence of existing predictors.
In statistical prediction, the following three cross-validation methods are often used to examine a predictor for its anticipated accuracy: independent dataset test, subsampling (K-fold cross-validation) test, and jackknife test [78].
Many reports suggest that RTE is an independent predictor for malignancy, with accuracy results exceeding conventional US criteria.
In statistical prediction, three cross-validation methods are often used to examine a predictor for its anticipated accuracy: K-fold cross-validation test, independent dataset test, and jackknife test [ 3].
Predictor accuracy for the training group was expressed using predicted and observed values for SSR status and modified GCS in determining weaning outcome.
To evaluate the performance of the predictor
Using these 47 features, the performance of the predictor was better than that of the predictor using all 340 features, with the results for accuracy, sensitivity, specificity, and MCC increasing to 86.62%, 78.34%, 94.91%, and 0.737, respectively.
