Prescriptive analysis

The primary goal of this is to answer the question How can I influence the output of the system? In order to avoid confusion, it's preferable to translate this concept into pure machine learning language, hence the question could be Which input values are necessary to obtain a specific output?

As discussed in the previous section, this phase is often merged together with predictive analysis because the models are generally employed for both tasks. However, there are specific situations where the prediction is limited to a null-input evolution (such as in the temperature example) and more complex models must be analyzed in the prescriptive stage. The main reason resides in the ability to control all the causes that are responsible for a specific output.

Sometimes, when not necessary, they are only superficially analyzed. It can happen either when the causes are not controllable (for example, meteorological events), or when it's simpler to include a global latent parameter set. The latter option is very common in machine learning and many algorithms have been developed to work efficiently with the presence of latent factors (for example, EM or SVD recommendation systems). For this reason, we are not focusing on this particular aspect, (which is extremely important in system theory) and, at the same time, we are implicitly assuming that our models provide the ability to investigate many possible outputs resulting from different inputs.

For example, in deep learning, it's possible to create inverse models that produce saliency maps of the input space, forcing a specific output class. Considering the example of the portrait classifier, we could be interested in discovering which visual elements influence the output of a class. Diagnostic analysis is generally ineffective because the causes are extremely complex and their level is too low (for example, the shape of a contour). Therefore, inverse models can help solve the prescriptive problem by showing the influence of different geometric regions. However, a complete prescriptive analysis is beyond the scope of this book and, in many cases, it's not necessary, hence we are not considering such a step in upcoming chapters. Let's now analyze the different types of machine learning algorithm.