Sentence examples for training sizes from inspiring English sources

SVM gets better results with smaller training sets, whereas RF becomes better at training sizes larger than 7 8% of the total.

Besides, we assess the performance of ten representative MOGPs thoroughly on eight examples in symmetric/asymmetric scenarios by considering, e.g., different training data (heterotopic or isotopic), different training sizes (small, moderate and large), different output correlations (low or high), and different output sizes (up to four outputs).

This is evidenced by the fact that the curves for training sizes of 20%, 400 %, and 60%% are all overlapping.

The conditions under which small training sizes retain full accuracy or possibly increase it, were not explored here since our main purpose was to select a sample size that was dictated by a given phenotyping budget.

Because the number of fathers, half and full sibs of validation bulls was identical for both training sizes analyzed, accuracy of GEBVs for the yield traits decreased by only 0.03 to 0.05 for a max values = 0.6 and 0.49.

The loss in accuracy with decreasing a max was similar for both training sizes from a max = 0.49 to 0.249, but considerably larger from 0.249 to 0.1249 with only 1,048 training bulls.

Experiments on various datasets verify the performance of error bound analysis and training size determination.

As seen from Table 6, sCCLTP is showing improved performance as the training size grows.

Thus, we avoided bias toward the class with the largest training size.

The boxplots show the variance calculated from the ten runs for each training size.

Performance suffers only marginally if we reduce the training size to 5,000, but significantly decreases in a smaller training set.