We consider the problem of network connectivity as a binary classification problem:
You must return a numerical score between 0 and 1 indicating your confidence that there is a connection, higher values indicating a more likely connection. The results of classification, obtained by thresholding the prediction score, may be represented in a confusion matrix, where tp (true positive), fn (false negative), tn (true negative) and fp (false positive) represent the number of examples falling into each possible outcome:
We define the sensitivity (also called true positive rate or hit rate) and the specificity (true negative rate) as:
The prediction results are evaluated with the so-called Area Under ROC Curve (AUC), which we refer to as AUC. It corresponds to the area under the curve obtained by plotting the "True positive ratio" against the "False positive ratio" by varying a threshold on the prediction values to determine the classification result. The AUC is calculated using the trapezoid method.
The submission file should contain 2 columns:
A sample submission file is provided. The file should contain the concatenation of the results on the validation and test data:
NET_neuronI_neuronJ,StrengthDownload a sample submission.