# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement  
We all understand that the idea of ML is based on induction and synthesis, which simulates human thinking and learning in the real world and describes a system's capacity to learn from training data for a specific problem to automate the process of analyzing model building and solving related tasks [17; 18; 19]. In Figure 1, we compare how humans think to machine learning. Machine learning training and forecasting are also appropriate for human induction and forecasting. In ML, we do not require any special problem related codes; instead, we just need to understand the appropriate concepts. It can be easily understood as a general algorithm that bases its reasoning on the data it receives as input. Regression, classification, and structural ML models can all be categorized. The classification problem in the ML algorithm separates the data into various groups by a particular connection. Common spam and handwritten digit recognition have a wide range of potential uses. The machine learning algorithm can be thought of as a \"black box\" that we can utilize to accomplish our objectives without having to know its intentions and goals. Therefore, choosing a good learning algorithm for a certain domain target application is difficult. The rationale is that various learning algorithms serve various purposes. The outcomes will vary even for various learning algorithms in the same category because of varied data properties [20]. ML can use nonlinear examples from the environment to forecast network traffic. Linear and nonlinear models can be broadly categorized under the maximum-likelihood model.  
ML-based algorithms can be split into supervised and unsupervised learning techniques. Classification and regression typically involve supervised learning, which modifies the classifier's parameters to achieve the desired performance using a set of samples from known categories. From the labeled training data, we infer a function. Unsupervised learning establishes associations between them by using categorized data [21]. We discover the model using the unlabeled data and describe the category, transformation, or probability of the data using data that was collected organically. Compressing data is the essential tenet. We may also receive semi-supervised and reinforcement learning methods by using the maximum likelihood algorithm by various training methods. To address the issue of insufficient labeled data, the semi-supervised learning method uses a large number of unlabeled samples and a small number of labeled samples. Reinforcement learning differs from the first three approaches in that feedback cannot be received right away during training, and the input is sequential data that does not satisfy the independent distribution. Agents can benefit from reinforcement learning by developing techniques to help them interact with complex surroundings to their fullest potential. Transfer learning is another teaching strategy applied in ML. The term \"transfer learning\" describes how one type of learning affects another type of learning or how the experience gained through learning might affect how well one completes other tasks.  
Many researchers have debugged and improved ML algorithms, which have been widely utilized in classification and prediction issues and have demonstrated great performance in a variety of disciplines. Based on a classification of different ML algorithms, we examine how various ML techniques might be used to network feature measurement in this article. In Table 1, we outline the uses and performance evaluation of these algorithms.  
Figure 1: Comparison of ML and human thought.