Supervised classification involves two main phases: training and testing. During the training phase, the algorithm learns from a labeled dataset that contains input features and corresponding output labels. The goal is for the algorithm to learn the relationship between the features and the labels. In the testing phase, the trained model is evaluated on new data to assess its predictive performance.
