Classification
A type of supervised learning task where the model predicts which category or class an input belongs to. The output is a discrete label rather than a continuous value.
Why It Matters
Classification is one of the most common ML applications — from spam detection and medical diagnosis to sentiment analysis and image recognition.
Example
An email spam filter classifying each incoming email as 'spam' or 'not spam', or a medical AI classifying skin lesions as 'benign' or 'malignant'.
Think of it like...
Like a mail sorter at the post office who looks at each letter and puts it in the correct bin based on the zip code — each item goes into exactly one category.
Related Terms
Regression
A type of supervised learning task where the model predicts a continuous numerical value rather than a discrete category. The output can be any number within a range.
Logistic Regression
A classification algorithm that uses the sigmoid function to predict the probability of a binary outcome. Despite its name containing 'regression,' it is used for classification tasks.
Decision Tree
A supervised learning algorithm that makes predictions by learning a series of if-then-else decision rules from the data. It creates a tree-like structure where each internal node tests a feature and each leaf provides a prediction.
Random Forest
An ensemble learning method that builds multiple decision trees during training and outputs the majority vote (classification) or average prediction (regression) of all the trees. The 'forest' of diverse trees is more robust than any single tree.
Support Vector Machine
A classification algorithm that finds the optimal hyperplane (decision boundary) that maximizes the margin between different classes. SVMs are effective in high-dimensional spaces.