What is Binary Classification ?

Binary classification is a classification task where a model chooses between two possible classes.

Examples include:

• Spam or not spam
• Fraud or legitimate
• Disease present or absent
• Customer churn or no churn

The two classes are often represented as:

0 = negative class
1 = positive class

How it works

A neural network typically produces a score for the positive class. That score can be converted into a value between 0 and 1 and compared with a threshold.

For example:

Predicted probability = 0.82
Threshold = 0.50

0.82 > 0.50 → Positive class

The threshold does not always have to be 0.5; it can be adjusted depending on the cost of false positives and false negatives.

Common setup in deep learning

Binary classification commonly uses:

• A single output score
• The sigmoid activation function to produce a probability-like value
• Binary cross-entropy as the training loss

For the activation itself, see “What is the sigmoid activation function?”. Loss functions are introduced in “What is a loss function in deep learning?”.

Binary vs multiclass classification

Binary classification has exactly two classes. If a model must choose one class from three or more mutually exclusive classes, that is a multiclass classification problem.

In short, binary classification is the task of assigning an input to one of two possible classes, often using a probability-like score and a decision threshold.