Sentence examples for best models were based from inspiring English sources

The evaluation of the performance of each semivariogram model and the selection of the best models were based on cross-validation, which estimates the reduced average error (RAE) and the standard deviation of the reduced average error (SRE) (Yamamoto and Landim 2013).

Lewis-Beck argued that the "the best models are based on theory... things that we know [or] that we're pretty certain we know," which in this case means the belief that "people vote about the main direction of the economy, and they vote about big macro political issues," especially in midterm elections.

From the simulated trait trajectories, and because the best model was based on BM, the increase in trait range can be symmetric (positive or negative axis).

Computational models for ADME properties work best when the models are based on single mechanism experimental assays.

The identification of the best domain and item level models was based on an examination of all goodness of fit and predictive performance statistics.

Each dataset was cross-validated ten-fold using 10 stratified subsets, and the best models were selected based on maximum precision and recall.

The best models were selected based on Akaike's Information Criterion corrected for small sample size (AICc, Burnham and Anderson 2007) using AICcmodavg (Mazerolle 2011) in R. Models with a difference in AICc of <2 were considered equally appropriate.

Interactions among all factor combinations were examined for significance along with temporal autocorrelation, and the best models were chosen based on Akaike Information Criterion with a second order correction for small sample sizes (AICc) (Burnham and Anderson 2002) and R values.

The selection of the best hypothesis model was based on the total cost, predictive ability, difference in the cost from the null hypothesis and alignment of the training set compounds on to the hypothesis.

In confirmatory factor analysis, the selection of the best fitting model was based on several fit indices.

Selection of the best evolutionary model is based on the same AICc approach described above for model-selection of lineage accumulation.