We measured prediction accuracy for each pair of 30 training-testing datasets and for a total of 30 predictions (Table 6).
Leave-one-complex-out validation is performed by removing all training examples of a PPI complex from the training set and then using these examples as a test set resulting in 39 unique train-test datasets.
These features are scaled using the ranges of the positive training data for both the training and test datasets and then fed to an SVM classifier.
Table 1 describes the development, training, and test datasets.
Split the dataset into training and test datasets for evaluating the predicted performance of the model.
According to previous studies, the S&P dataset is divided into training and test datasets.
The F-score performance on the training and test datasets are fully shown in Figure4c,d, respectively.
Table 1 shows the information of the training and test datasets.
1. Split the dataset into training and test datasets for evaluating the predicted performance of the model.
Training and test datasets for our experiments (see Figure 12) were taken from Middlebury Evaluation Testbench [12, 24, 25].
In Section 6.1, we describe the training and test datasets and further details on the required acoustic models.
