## 1 Introduction  
Over the last decades, the demand for traffic and bandwidth in optical backbone networks has constantly increased. Therefore, there is a need for efficient use of available network resources. Apart from introducing new technological solutions enabling higher transmission rates, a network operator needs to frequently reconfigure and reoptimize the network in order to minimize capital expenditure (Capex) and operational expenditure (Opex) [1; 2; 3; 4; 5]. Efficient use of available resources is of particular importance for dense wavelength division multiplexed (DWDM) networks [6]. With their high bandwidth and low cost, DWDM optical networks form the platform for implementing core physical infrastructure by all major operators worldwide. They are currently penetrating important new telecoms markets, such as the the data market and the access segment. Consequently, DWDM network management is one of the main problems faced by optical network operators whereby an accurate assessment of transmission quality is a necessary prerequisite to any realistic network reconfiguration plan. In this paper, we have therefore focused on the problem of quality assessment in a DWDM network and propose a solution, which can be relatively easily implemented by a network operator. This solution involves applying machine learning algorithms to a database that contains relevant information about the network traffic and can be conveniently extracted from the DWDM system via the control plane. More specifically, this paper applies machine learning (ML) algorithms to a database that has been taken directly by the control plane from a next-generation optical network from a large telecom network operator within the software-defined network (SDN) paradigm. ML techniques may prove helpful in improving the management of resources within a modern optical network mainly because of their ever increasing complexity of infrastructure. An intelligent selection of the parameter values may improve the overall network performance. The use of data directly from an operational optical network, collected through a control plane and as input to ML algorithms is, not straightforward due to the large data size and class imbalance. This class imbalance arises because practically all links are working in an operational optical network, and nobody attempts to set up or especially keep allocated a link that, for instance, has the bit error rate too high. Even though there is some knowledge regarding nonworking links within a corporation, it is usually very scarce and poorly documented. Furthermore, experiments on synthetic data may lead to overoptimistic predictive performance estimates as well as a failure to identify important obstacles. Therefore, we propose a specially tailored approach using ML algorithms to solve this problem and compare binary classification algorithms combined with different class imbalance compensation techniques with single-class classification algorithms. In our solution, all attributes are divided into subsets, and ML algorithms are compared for each subset of parameters. As the measure of performance of selected machine learning algorithms, receiver operating characteristics (ROC) and precision-recall curves were selected. When compared with [7; 8], this study presents a comprehensive approach to investigate the prediction quality of machine learning algorithms applied to optical network design. We used an extended set of classification algorithms and a combined dataset originating from two networks. We systematically investigated the predictive utility of multiple attribute subsets, differing in the scope of physical properties of network paths, and performed hyperparameter tuning to ensure that each algorithm reveals its full predictive power potential. The paper is organized as follows. Section 2 provides a review of the literature and papers in this scientific field. Section 3 presents the data collection methodology, while Section 4 describes the figures of merit of machine learning algorithms.