Classification, in the context of machine learning and statistics, is the task of assigning a category or class label to a given input data point based on its features. It is a type of supervised learning, meaning that the algorithm learns from a labeled training dataset where the correct class labels are already known.

Here's a breakdown of key aspects:

• Input Data: The input data consists of features or attributes that describe the characteristics of the data point.
• Classes/Categories: These are the predefined groups or labels that the data points can belong to. For example, in image classification, the classes might be "cat," "dog," or "bird."
• Training Data: A labeled dataset is used to train the classification model. Each data point in the training set has known features and its corresponding class label.
• Classification Model: An algorithm that learns the relationship between the input features and the class labels from the training data.
• Prediction: Once the model is trained, it can be used to predict the class label for new, unseen data points based on their features.

Examples of Classification Tasks:

• Email Spam Detection: Classifying emails as either "spam" or "not spam."
• Image Recognition: Identifying objects in images (e.g., classifying images as containing a "car," "person," or "tree").
• Medical Diagnosis: Determining whether a patient has a certain disease based on their symptoms and medical test results.
• Credit Risk Assessment: Assessing the likelihood of a loan applicant defaulting on their loan.

Common Classification Algorithms:

• Logistic Regression
• Support Vector Machines (SVM)
• Decision Trees
• Random Forest
• Naive Bayes
• K-Nearest Neighbors (KNN)
• Neural Networks