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In the case of ANN modes, both the local ensemble and the local models perform almost equally in terms of correlation coefficient values achieved.
In terms of the mean training error, the two models perform almost identically.
The two models perform almost equally well when comparing the total number of correct predictions (species in the woodland pool correctly predicted as present plus the number correctly predicted as absent; Table 1).
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All the models performed almost equally.
All independent models performed almost equally, with the predictors explaining, on average, 98% of the variation in the BGB.
Because all fitted independent models performed almost equally, the models using either DBH or RCD exclusively are preferred as tree height is difficult to measure in natural forests.
Based on the fact that all models performed almost equally, the models using either diameter at breast height (DBH) exclusively as a predictor should be preferred, as tree height is difficult to measure.
Because all the fitted independent models performed almost equally, any model can be used accurately to estimate BGB and carbon stocks in A. johnsonii stands (mecrusse woodlands), which, along with mopane and miombo are the most important woodlands in Gaza and Inhambane Provinces.
This analysis was limited by sample size, but we found that a two-miRNA multivariate model performed almost as well as the optimal averaged univariate models.
When compared to the previously computed SENSOR model, which is based merely on accelerometer sensor data and overall activity levels, we can state that this model performs almost equally well than the CONV model.
Which course models perform most effectively online?
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