# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement  
In the process of supervised learning, input data are mapped to tags, prediction models are trained using data and their matching tags, and networks are trained using known examples. Depending on the quantity of annotated training data available [32], supervised learning is frequently employed in applications such as spam detection, facial recognition, and text classification. The objective is to increase the model's capacity for generalization in a particular area. Algorithms for classification and regression are examples of supervised learning techniques. The regression process produces continuous data, whereas the classification technique produces discrete data. The classification algorithm's objective is to categorize the data in the set, and the regression algorithm's objective is to find the best fitting line between these data so that every point in the dataset can be as closely related to it as possible.  
The regression algorithm is one of the simplest and most common machine learning algorithms. One of the simplest and most used machine learning algorithms is regression [33] and the assessment index used in this mathematical technique for performing the predictive analysis is the discrepancy between the expected and actual values. To calculate the network quality of service, we used the return to estimate the packet loss rate and other network properties. Additionally, the distinctive correlations between application KPIs and network indicators can be inferred using multiple linear returns [34]. This study focuses on the use of classification algorithms in network measurement because there are few studies on the use of regression techniques in network measurement. The classification algorithm's job is to classify samples into relevant groups based on those groups' traits. The most popular classification algorithms include Bayes, ANN, decision trees, linear regression, and decision trees.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm Category** & **Algorithm** & **Application** & **Ref** & **Performance Analysis** \\ \hline Random forest & Packet loss & [22] & The training period is brief and can result in accurate estimation \\ \hline ANN & throughput & [23] & High level of accuracy in forecasting end-user throughput in LTE and related 5G networks \\ \hline SVM & throughput & [24] & When compared to historical methods, the accuracy rate is over three times higher \\ \hline DNN & Path delay & [9] & To increase prediction accuracy, fine-tune several super parameters \\ \hline Supervised & DT & Path delay & [25] & A high prediction accuracy and the ability to be applied to large-scale systems \\ \hline FNN & Bandwidth & [26] & Substantial data savings \\ \hline RNN & Path delay & [27] & Find the algorithm with the most accurate prediction \\ \hline CNN & Path Delay & [28] & This technique’s estimation accuracy clearly outperforms that of PathQuick3 \\ \hline RBF & Path loss & [21] & The mistake is relatively modest, and the estimated route loss is close to the true value \\ \hline MLP NN & Path loss & [8] & The MLP neural network model combined with the RBF neural network is shown to have the best performance \\ \hline Unsupervised & LSTM & Bandwidth & [29] & Find a more accurate prediction method in the two modes \\ \hline Reinforcement learning & K-Means & Bandwidth & [29] & More storage space enhances the classification system \\ \hline Transfer learning & Greedy algorithm & Bandwidth & [30] & With less unpredictability, the available bandwidth can be anticipated correctly \\ \hline \hline \end{tabular}
\end{table}
Table 1: Application of Network Measurement and analysis of ML algorithms.