cting to a situation after the fact. For example, raising an alarm after an intruder burgled a house when a system shuts down due to a security threat or hardware failure. Although reactive analytics can help to explain what happened to an event, it does not avoid the accident or failure. Traditional rule-based or statistical methods such as clustering can be used for reactive analytics. Predictive analytics is a more sophisticated form of analytics that involves predicting an event or a future state of a system based on historical data or observations. Predictive analytics can be unsu- pervised, supervised, or semi-supervised as will be explained in the next section. Algorithms for predictive analytics are either linear in nature, such as those using regression methods, or nonlin- ear, based on ML methods, such as neural network, support vector machines, or boosting [Kot07]. Examples of applications using predictive analytics include estimating the future value of an investment portfolio and the sentiment, emotion, or retention rate of users based on their historical interactions. Prescriptive analytics is closer to AI where the goal is to identify an optimized sequence of steps or actions to take. Prescriptive analytics can be data-powered and may involve advanced ML models to perform optimization of the best-possible outcome. The nature of this analytics also requires the system to continuously be dynamic and reexamine its sequence of decisions as the context changes. An example of prescriptive analytics is applied in autonomous vehicles where the system will need to continuously optimize its decisions (turns, signals, etc.). Another example that is relevant to SDN and 5G is optimizing data routing across all network nodes and end devices as traffic changes rapidly. 8.2.5.2 Supervised versus Unsupervised Learning Predictive and prescriptive analytics can be either supervised or unsupervised. Supervised learning implies that the training process has access to the correct targets or desired outputs, while unsupe