Sentence examples for model building set from inspiring English sources

Using a testing set of 245 samples, approximately twice the size of the model building set, the classifier had 91.3% sensitivity and 88.5% specificity.

Thus, despite the much smaller size of the validation sample set, results similar to those from the model building set support the overall conclusion that 5-year and lifetime risk estimations for the PFRM are significantly improved over the BCRAT.

This is likely due to a combination of many factors, including; 1) 1.2 * 10 sequences contained less than 100 amino acids, 2) sequencing and assembly errors, 3) incorrect translations of DNA, and 4) 10 μm filter removing the majority of organisms that are represented in the model building set.

This is especially the case if all compounds of an entire region of the data set are removed from the model building set and split away into the test set.

The validation set is a smaller sample and as might be expected there is some variability in the OR values compared to the larger model building set (Fig. 2, lower vs. upper panels).

This dataset was divided randomly into a training set comprising two thirds of individuals ("hospital 1-model building set") and a testing set of the remaining third ("hospital 1-testing set").

The model-building set was then used to establish a tree that was pruned by use of the validation set to achieve an estimation of the most appropriate tree through minimal cost-complexity pruning [ 20, 21].

As a check for cross-validation, the accuracy with which the models predicted the perceived impact subscale values using the model building data set was compared to the accuracy achieved with the testing data set.

Using the parameters established with the model building data set, agreement (or not agreement) to a perceived impact subscale was predicted for the testing data set.

The initial model building study set consisted of 5022 Caucasian females: 1671 breast cancer cases and 3351 age-matched cancer-free controls, and the validation study set consisted of 1193 Caucasian females: 400 breast cancer cases and 793 age-matched cancer-free controls.

The predictive models were built using a model building data set (299 randomly selected interviews).