Defining the ML problem
Before we get into the details of ML, it is important to note that not all problems are appropriate to be solved with ML. For a problem to be a good candidate, it should have the following characteristics (we will focus only on supervised learning (SL) problems for now):
- There is a clear target or label value that would be useful if an algorithm can predict it. In the absence of an algorithm this value remains unknown, requires a person's judgment, or requires substantial effort for it to be determined. Sometimes, the target will not be the actual variable of interest but a critical component of that calculation. This part is not always obvious, but the problem analysis you did in previous sections of this chapter will certainly help in clarifying which variable makes the best target.
- You have access to a large enough historical dataset that contains the values of the target or label you wish to predict. You will need to create a list of data...
