Machine Learning Foundations

Binary Classification, explained

Binary classification is a prediction task where each example belongs to one of two classes.
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Part of the Semantic Notion technical glossary.

Short Definition

Binary classification is a prediction task where each example belongs to one of two classes.

Intuition

The model answers a yes-or-no question, such as fraud or not fraud, spam or not spam, approved or rejected.

Technical Definition

Binary classification estimates a decision boundary or probability for two mutually exclusive target classes.

Example

A content moderation model may classify a message as policy-violating or allowed.

Common Misunderstandings

Binary classification still needs threshold selection.
A probability score and a final class decision are different outputs.

Same Category

More from Machine Learning Foundations

BackpropagationBackpropagation is the algorithm used to compute gradients through a neural network efficiently.Bias-Variance TradeoffThe bias-variance tradeoff describes the balance between models that are too simple to capture structure and models that are too sensitive to training data noise.DropoutDropout is a regularization technique that randomly disables parts of a neural network during training.FeatureA feature is an input signal used by a model to make a prediction or learn a representation.Feature EngineeringFeature engineering is the process of transforming raw data into model inputs that make useful patterns easier to learn.GeneralizationGeneralization is a model's ability to perform well on new data, not just the data used during training.

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