Classification in Machine Learning

Classification in Machine Learning refers to the process of predicting the categorical label of new observations based on past observations with known labels. In this supervised learning approach, the model is trained on a labeled dataset where each instance has a corresponding category, enabling the algorithm to learn the relationship between features and their respective classes.

Classification tasks can be binary (two classes) or multiclass (more than two classes). The primary goal is to assign the correct label to new, unseen data based on the learned patterns.

Examples:

• Email Spam Detection: Classifying emails as “spam” or “not spam” based on features such as the sender, subject line, and content.
• Image Recognition: Identifying objects within images, such as classifying images as “cat,” “dog,” or “bird” based on pixel values and patterns.
• Sentiment Analysis: Determining the sentiment of text data, categorizing it as “positive,” “negative,” or “neutral.”

Cases:

• Medical Diagnosis: Classifying patients as “healthy” or “diseased” based on medical tests and history.
• Credit Scoring: Assessing whether an individual is likely to default on a loan, categorizing applicants into “low risk” or “high risk.”
• Customer Segmentation: Grouping customers into segments like “loyal,” “new,” or “at-risk” based on purchasing behavior.