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Furthermore, the above relationship between the hiPSCs and the parental SCs was verified by estimating the classification accuracy by leave-one-out cross-validation (LOOCV) on the nearest-neighbor classifier, based on Pearson's correlation distance (see additional file 5: Cross validation of cell classification).
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We increased the accuracy of the classification by estimating the ratio of the width to the height of the headlight and the grill.
Second, these results unpack the generic effect of dual use by estimating the associations based on dual use classifications involving only inpatient, only outpatient, or both service types (Model 2 in Table 2).
We build on this finding by estimating the effect of cause of death on death certificate racial classification in two sets of logistic regression models (Table 1).
Breiman proposed the "out-of-bag" method for estimating statistics of bagged classifiers, which was subsequently applied by other authors to estimate the classification error.
We estimated the classification performance by using 10-times-repeated fivefold cross validation (10 × 5-CV) and leave-pair-out (LPO) bootstrapping [ 11] (a method that better accounts for training and testing variability).
Estimate the classification map R x, y) in (24) 4.
Two approaches were used to estimate the classification performance.
The thickness of each individual atomic column is estimated by the classification of its HAADF integrated intensity using a Gaussian mixture model.
(A posterior probability of a testing data point, A, is estimated by the classification model as the probability that A will be classified as positive, denoted as P(+|A).) As the probability getting lower, the Naïve Bayesain classifier outperforms the SVM classifier, with a larger area under curve.
A performance of the discriminant criterion was evaluated by estimating error rates in the classification of future observations.
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