Create multiclass classification models
It's also possible to create multiclass classification models, in which there are more than two possible classes. For example, the health clinic might expand the diabetes model to classify patients as:
- Non-diabetic
- Type-1 diabetic
- Type-2 diabetic
The individual class probability values would still add up to a total of 1, because a patient can fall into only one of the three classes, and the most probable class would be predicted by the model.
About multiclass classification models
Multiclass classification can be thought of as a combination of multiple binary classifiers. There are two ways in which you can approach the problem:
One-vs-Rest (OVR), in which a classifier is created for each possible class value, with a positive outcome for cases where the prediction is a certain class, and a negative outcome for cases where the prediction is any other class. For example, a classification problem with four possible shape classes (square, circle, triangle, hexagon) would require four classifiers that predict:
- Square or not
- Circle or not
- Triangle or not
- Hexagon or not
One-vs-One (OVO), in which a classifier for each possible pair of classes is created. The classification problem with four shape classes would require the following binary classifiers:
- Square or circle
- Square or triangle
- Square or hexagon
- Circle or triangle
- Circle or hexagon
- Triangle or hexagon
In both approaches, the overall model must take into account all these predictions to determine which single category the item belongs to.
Fortunately, in most machine-learning frameworks, including tidymodels, implementing a multiclass classification model is not significantly more complex than implementing a binary classification.