Algorithms such as Naive Bayes and Support Vector Machines can be used for classification. The classification model could be a function that maps from an email text to a spam classification (or non-spam classification).
Customer behavior prediction: Customers can be classified into different categories based on their buying patterns, web store browsing patterns etc.Here is the list of real-life examples of machine learning classification problems: The classification models are trained using some of the following algorithms:Ĭlassification Problems Real-world Examples
Multinomial classification: Classifies data into three or more classes Document classification, product catgeorization, malware classificationĬlassification problems are supervised learning problems wherein the training data set consists of data related to independent and response variables (label).
Binary classification – Classifies data into two classes such as Yes / No, good / bad, high / low, suffers from a particular disease or not etc.For example, whether a person is suffering from a disease X (answer in Yes or No) can be termed as a classification problem.Ĭlassification problems can be of the following different types: Machine learning classification problems are those which require the given data set to be classified in two or more categories. Classification Problems Real-world Examples.