As we discussed in the last chapter, supervised learning is the machine learning process of leveraging a function that maps an input to an output based on example input-output pairs, inferring a function from labeled training data comprising a set of training samples.
Again, in the last chapter, we saw how, when using the model builder, we could set a label column for a predictive model to predict. Recall that, in one example, we chose the column IS_TENT from within the training data for the model to predict.
Now, in this section of this chapter, we want to examine scenarios where we have no label data defined in our data, or in other words, unsupervised learning problems. To reiterate, in these cases, we have no feedback (or label) based on the prior prediction results available; we expect to solve these cases without indicating or setting a desired label...
