Binary classification
term_id: binary_classification
Category: basic_concepts
Definition
Binary classification is a fundamental machine learning problem where the output variable is categorical with exactly two possible outcomes, such as true/false or spam/not spam. Algorithms like logistic regression, support vector machines, and decision trees are commonly used. The model learns a decision boundary that separates the two classes based on training data features, enabling predictions for new, unseen instances.
Summary
A supervised learning task where the goal is to predict one of two possible classes for each input instance.
Key Concepts
- Decision Boundary
- Confusion Matrix
- Logistic Regression
- Accuracy Metrics
Use Cases
- Spam Detection
- Medical Diagnosis
- Fraud Detection