Sentence examples for validation in which from inspiring English sources

The third stage was validation, in which psychometric reliability and validity analyses were conducted.

Maya's built-in checking of the data type and printing an error message is a simple type of input validation, in which inputs are checked to make sure that they are meaningful in context.

Use of external validation, in which a few sedimentary samples are added to the validation set, coupled with choice of the first local minimum in the root mean square value in all the components of the validation model, provides optimal results in this data set.

LDA was run with a leave-one-out cross validation, in which the priors were adjusted to give an equal probability for each species.

After this, we have used tenfold cross validation in which a Naive Bayes and a support vector machine have been trained and evaluated.

For the sake of comparability, we used the same evaluation scheme, applying leave-one-out (LOO) cross validation, in which we trained our model with sequences of eight actors and evaluated our proposed method with the sequences of the remaining one actor.

To provide further evidence that the ensembles of structures produced here are valid representations of the experimental ensembles, cross-validation, in which only 80% of the PRE-derived distance restraints were used in the PRE-RMD calculations ("working") and the remaining 20% provide a "free" data set whose satisfaction is not preordained by their inclusion as restraints, was conducted.

An alternative, completely empirical approach to assessing aggregate predictive uncertainty is cross-validation, in which each compound in the training set is held back in turn [24].

Some workers prefer to use "leave-some-out" cross-validation – in which several compounds are held back together – to address this problem.

This is commonly done using n-fold cross-validation in which the training set is split into n parts (often 10) and each of these parts is used as reference once, while the remaining parts in each such iteration are merged together as the training set.

In the second part of the experiment, classifiers were trained and tested on the test database only, using 10-fold cross-validation in which each classifier was trained on 9/10 of the test database, and its precision was evaluated on the remaining 1/10.