In this post, we are going to dive into the concepts of Supervised Learning or rather known as Classification in the domain of Machine Learning. We will discuss the definitions, components, examples of classification.
Classification can be defined as the task of learning a target function **f**that maps each attribute set **x**to one of the predefined labels y.
**Example: **Assigning a piece of news to one of the predefined categories.
In the community of Data Science or Machine Learning, anything done on data is called **modelling. **In context of classification, there are two types of modelling:
- Descriptive Modelling: A classification model can serve as an explanatory tool to distinguish between objects of different classes. **Example: **A model that defines the type of vertebrae based on its features.
- _Predictive Modelling: _A classification model can also be used to predict the class label of unknown records.
Classification techniques are most suited for predicting or describing data sets with binary or nominal categories. They are less effective for ordinal categories (e.g.,to classify a person as a member of high-, medium-, or low- income group) because they do not consider the implicit order among the categories.
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