Classification algorithm in machine learning

Classification algorithm in machine learning
Supervised machine learning algorithms can be broadly divided into regression and classification algorithms. In regression algorithms, we predicted the output for continuous values, but to predict categorical values, we need classification algorithms. We can see the difference between regression and classification algorithms in the picture below.



What is a classification algorithm?
Classification is the process of finding a function that helps in dividing a data set into classes based on various parameters. In classification, a computer program is trained on a training data set, and based on that training, divides the data into different classes. For example, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called targets/labels or categories.
Unlike regression, the output variable of classification is a category, not a value, such as "Green or blue", "fruit or animal", etc. Since the classification algorithm is a supervised learning method, it takes labeled input data, which means it contains an input with a corresponding output.
The main purpose of a classification algorithm is to determine the category of a given data set, and these algorithms are mainly used to predict the output of categorical data.
Classification algorithms can be better understood with the help of the diagram below. In the diagram below, there are two classes, Class A and Class B. These classes have properties similar to each other and different from other classes.

An algorithm that performs classification on a data set is known as a classifier. There are two types of classification:

• 
  Binary classifier: If a classification problem has only two possible outcomes, it is called a Binary classifier.
  Examples: yes or no, male or female, spam or not spam, cat or dog, etc.
  
• 
  Multi-Class Classifier: If a classification problem has more than two outputs, it is called a Multi-Class Classifier.