What are classification problems?

Classification problems are the problems in which an object is to be classified in one of the n classes based on the similarity index of its features with that of each class. By classes, we mean a collection of similar objects. The objects are said to be similar on the basis of matching features, e.g., color, shape, size, etc. The classes are identified on the basis of their unique labels.

Example

Consider an example of three containers. Containers 1, 2, and 3 have red, blue, and green balls respectively. Let’s say we get a new ball and are asked to place the ball in the container it belongs to. The problem here is a classification problem as we have to classify which container the ball belongs to. We will place the ball in a container depending on its color. Let’s say the ball is red; it will be placed in a container already containing red balls.

Classification problems in Deep Learning

In Deep Learning, classification problems are solved by training classification models. The classification models are trained by providing objects and their labels. The models learn and identify similar features of objects in a class. After training, the model is tested on a separate data it was trained. For testing, only the object to classify is given without its label. The classification model predicts the label of the object. The accuracy of the model is determined on the basis of correctly predicted labels.

Types of classification problems

• Binary Classification: The classification problems in which the number of classes is 2.
• Multi-Class Classification: The classification problems in which the number of classes is more than 2.
• Multi-Label Classification: The classification problems in which an object can belong to multiple classes.
• Imbalanced Classification: The classification problems in which the number of objects in the classes is imbalanced.