Classification
Classification is another type of supervised learning technique, where the task is to categorize a given dataset into different classes. Machine learning classifiers learn a mapping function from input parameters called Features that go to a discreet output parameter called Label. Here, the learning function tries to predict whether the label belongs to one of several known classes. The following diagram depicts the concept of classification:
Figure 7.2 – Logistic regression
In the preceding diagram, a logistic regression algorithm is learning a mapping function that divides the data points in a two-dimensional space into two distinct classes. The learning algorithm learns the coefficients of a Sigmoid function, which classifies a set of input parameters into one of two possible classes. This type of classification can be split into two distinct classes. This is known as binary classification or binomial classification.
