Classification is only one of the possible machine learning problems that can be addressed with scikit-learn. We can organize them in the following categories:
- In the previous example, we had a set of instances (that is, a set of data collected from a population) represented by certain features and with a particular target attribute. Supervised learning algorithms try to build a model from this data, which lets us predict the target attribute for new instances, knowing only these instance features. When the target class belongs to a discrete set (such as a list of flower species), we are facing a classification problem.
- Sometimes the class we want to predict, instead of belonging to a discrete set, ranges on a continuous set, such as the real number line. In this case, we are trying to solve a regression problem (the term was coined by Francis Galton, who observed that the heights of tall ancestors tend to regress down towards a normal value, the average human height). For example, we could try to predict the petal width based on the other three features. We will see that the methods used for regression are quite different from those used for classification.
- Another different type of machine learning problem is that of unsupervised learning. In this case, we do not have a target class to predict but instead want to group instances according to some similarity measure based on the available set of features. For example, suppose you have a dataset composed of e-mails and want to group them by their main topic (the task of grouping instances is called clustering). We can use it as features, for example, the different words used in each of them.
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