In the simplest terms, these algorithms help us answer objective questions or a yes-no prediction. For instance, these algorithms are useful in scenarios like is it going to rain today?, or can this tumour be cancerous?, and so on.

Formally, the key objective of classification algorithms is to predict output labels that are categorical in nature depending upon the input data points. The output labels are categorical in nature; namely, they each belong to a discrete class or category.

Logistic regression, Support Vector Machines (SVMs), Neural Networks, Random Forests, k-Nearest Neighbours (KNN), Decision Trees, and so on are some of the popular classification algorithms.

Suppose we have a real-world use case to evaluate different car models. To keep things simple, let's assume that the model is expected to predict an output for every car model as either acceptable or unacceptable based on multiple input training samples. The input training samples have attributes such as buying price, number of doors, capacity (in number of persons), and safety.

The level apart from the class label denotes each data point as either acceptable or unacceptable. The following diagram depicts the binary classification problem at hand. The classification algorithm takes the training samples as input to prepare a supervised model. This model is then utilized to predict the evaluation label for a new data point:

Since output labels are discrete classes in case of classification problems, if there are only two possible output classes the task is termed as a binary classification problem, and a multi-class classification otherwise. Predicting whether it will rain tomorrow or not would be a binary classification problem (with output being a yes or a no) while predicting a numeric digit from scanned handwritten images would be multi-class classification with 10 labels (zero to nine possible output labels).