Finding a function to divide the dataset into classes based on several parameters is the process of classification. In classification, data is divided into various classes by a computer program that has been trained on the training dataset.
Finding the mapping function to convert the input (x) to the discrete output is the goal of the classification algorithm (y).
Example: Email spam detection offers the clearest illustration of the Classification issue. When a new email arrives, the model determines whether it is spam or not based on training data from millions of emails on various parameters. The email gets placed in the Spam folder if it is considered spam.
What is Classification?
On the basis of training data, the Classification algorithm is a Supervised Learning technique that is used to categorize new observations. In classification, a program makes use of the dataset or observations that are provided to learn how to categorize/classify fresh observations into various classes or groups. For instance, Animal or Bird, Male or Female, Yes or No, 0 or 1, Spam or Not Spam. Targets, labels, or categories can all be used to describe classes.
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