Supervised Learning Algorithms

Abstract

Supervised learning algorithms help the learning models to be trained efficiently, so that they can provide high classification accuracy. In general, the supervised learning algorithms support the search for optimal values for the model parameters by using large data sets without overfitting the model. Therefore, a careful design of the learning algorithms with systematic approaches is essential. The machine learning field suggests three phases for the design of a supervised learning algorithm: training phase, validation phase, and testing phase. Hence, it recommends three divisions (or subsets) of the data sets to carry out these tasks. It also suggests defining or selecting suitable performance evaluation metrics to train, validate, and test the supervised learning models. Therefore, the objectives of this chapter are to discuss these three phases of a supervised learning algorithm and the three performance evaluation metrics called domain division, classification accuracy, and oscillation characteristics. The chapter objectives include the introduction of five new performance evaluation metrics called delayed learning, sporadic learning, deteriorate learning, heedless learning, and stabilized learning, which can help to measure classification accuracy under oscillation characteristics.