Classification: from data to a trained model

The process of training a classification model from a source dataset is a multi-step affair that involves many steps. In this section, we will take a bird's eye view (depicted in Figure 11.1) of this whole process, which begins with a labeled training dataset (Figure 11.1 part A.).

Figure 11.1 – An overview of the supervised learning process that takes a labeled dataset and outputs a trained model

This training dataset is usually split into a training part, which will be fed into the training algorithm (Figure 11.1 part B.). The output of the training algorithm is a trained model (Figure 11.1 part C.). The trained model is then used to classify the testing dataset (Figure 11.1, part D.), originally set aside from the whole dataset. The performance of the model on the testing dataset is captured in a set of evaluation metrics that can be used to determine whether a model generalizes well enough to previously...