You have seen in the previous section how logistic regression can be used to perform binary classification. In this section, you will see how to use logistic regression (which is known to do the binary classification) for multiclass classification. The algorithm used is known as the "one-vs-all" method.

The algorithm is very intuitive. It learns many models as many different classes of items are there in the training dataset. Later, when a new entry is given for identification, all the models are used to compute the confidence score that reflects the confidence of the model that the new entry belongs to that class. The model with the highest confidence is selected.

In this example, you will see how Accord.NET can be used to implement multiclass classification to identify iris flowers. There are three types of iris flowers, namely, Iris versicolor, Iris setosa, and Iris virginica. The task is to identify a given flower from the measurements...