Scala and Spark for Big Data Analytics

Supervised learning

A supervised learning application makes predictions based on a set of examples, and the goal is to learn general rules that map inputs to outputs aligning with the real world. For example, a dataset for spam filtering usually contains spam messages as well as non-spam messages. Therefore, we are able to know whether messages in the training set are spam or ham. Nevertheless, we might have the opportunity to use this information to train our model in order to classify new unseen messages. The following figure shows the schematic diagram of supervised learning. After the algorithm has found the required patterns, those patterns can be used to make predictions for unlabeled test data. This is the most popular and useful type ...