The usual approach is to apply a nested cross-validation procedure: hyperparameter selection is performed in the inner cross-validation, while the outer cross-validation computes an unbiased estimate of the expected accuracy of the algorithm with cross-validation based hyperparameter tuning. The ...
To illustrate why this is happening, let’s use an example. Suppose that we are working on a machine learning task, in which we are selecting a model based onnrounds of hyperparameter optimization, and we do this by using a grid search and cross-validation. Now, if we are using the sa...
We test the performance of element tagging on ETIP dataset and report results using fivefold cross-validation testing on 150 labeled contracts. Table 3 shows the confusion matrix computed by our method denoted by CNN-SW with Jieba word segmentation. The confusion matrix has eight categories, where...
First, we apply nested cross validation to allow flexible and effective hyperparameter tuning while producing non-contaminated estimates of heldout model performance even on small trials datasets. Second, we use multiple performance metrics to assess both a model's ability to predict outcome and ...