## 3 Problem Formulation  
This work approaches transmission quality assessment as a binary classification task in which a network path is to be classified as \"good\" (successfully deployed, i.e., meeting transmission quality requirements or \"bad\" (not successfully deployed, i.e., not meeting transmission quality requirements). This makes it possible to apply machine learning algorithms to create models providing class or class probability predictions based on available path attributes.  
It is a well-established fact in the area of machine learning that a suitable selection of attributes may significantly improve the accuracy of predictions. In this contribution, therefore, the problem of attribute selection for machine learning algorithms used in models predicting the quality of transmission in optical point-to-point connections of a DWDM network is studied. Two approaches can potentially be considered when selecting attributes. One of them is quantitative in nature and is based on the classical approach used in machine learning that finds the attributes that are best correlated with the prediction results. The other is rather qualitative and aims to exploit the well-established optical transmission theory in optical networks. It has been observed that the presence of physical phenomena such as fiber attenuation, optical signal attenuation in a DWDM node, polarization-dependent loss, EDFA amplifier noise, chromatic dispersion, polarization dispersion, and nonlinear phenomena, including especially four-wave mixing, is related to the quality of transmission in the optical links of a point-to-point DWDM network. One would therefore expect that there exists a mapping that relates the parameters that characterize these physical phenomena and the quality of transmission in point-to-point DWDM network optical links. Considering such parameters as fiber attenuation, optical signal attenuation in a DWDM node, polarization-dependent loss, EDFA amplifier noise figure, chromatic dispersion, and polarization dispersion, the parameters characterizing nonlinear phenomena present in optical fibers (e.g., the nonlinear fiber coefficient present in the nonlinear Schrodinger equation) should therefore be instrumental in evaluating the quality of transmission in point-to-point DWDM network optical links. However, these parameters are not readily available to a DWDM network operator via the control plane. Further, in general, it is not a trivial matter to accurately extract the values of such a set of parameters from an operating DWDM network primarily because it would require a major effort and prompt a significant increase in operational expenditure (Opex), which would be extremely difficult to justify at a DWDM network operator management board level. Other methods, therefore, need to be considered. An approach that seems pragmatic and hence is investigated here consists in first collecting a set of parameters characterizing a DWDM network, which are readily available via the DWDM network control plane and thus can be used to automatically create a database that characterizes the considered DWDM network.  
In this study, the data were directly collected from the network monitoring system of the real optical network of a large telecom operator. It was assumed that the optical channels (lightpaths) operate in C-band. The network is located in Poland, with nodes corresponding to network elements located in the main cities of the country. The network is extensive, with an area of approximately 1000 km in diameter. The longest distance between the two most distant nodes is 120 km, and the average distance between them is 60 km.