Classical machine learning models
Classical machine learning models require pre-processed data in the form of tables and matrices. Classical machine learning models, such as random forest, linear regression, and support vector machines, require a clear set of predictors and classes to find patterns. Due to this, our pre-processing pipelines need to be manually designed for the task at hand.
From the user’s perspective, these systems are designed in a very classical way – there is a user interface, an engine for data processing (our classical machine learning model), and an output. This is depicted in Figure 9.1:
Figure 9.1 – Elements of a machine learning system
Figure 9.1 shows that there are three elements – the input prompt, the model, and the output. For most such systems, the input prompt is a set of properties that are provided for the model. The user fills in some sort of form and the system provides an answer. It...
