Model definition

If we wanted to summarize the machine learning process using just one word, it would certainly be models. This is because what we build with machine learning are abstractions or models representing and simplifying reality, allowing us to solve real-life problems based on a model that we have trained on.

The task of choosing which model to use is becoming increasingly difficult, given the increasing number of models appearing almost every day, but you can make general approximations by grouping methods by the type of task you want to perform and also the type of input data, so that the problem is simplified to a smaller set of options.

Asking ourselves the right questions

At the risk of generalizing too much, let's try to summarize a sample decision problem for a model:

• Are we trying to characterize data by simply grouping information based on its characteristics, without any or a few previous hints? This is the domain of clustering techniques.
• The first and most basic question...