# What is Logistic Regression? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Foundational AI  
> **Canonical Citation:** [https://spidits.com/ai-glossary/logistic-regression](https://spidits.com/ai-glossary/logistic-regression)

## Definition
Logistic Regression is a foundational classification algorithm used to predict the probability of a binary target variable by mapping linear inputs to a sigmoid probability curve.

## Architectural Mechanics & Deep Dive
Logistic regression is a classification algorithm used to predict the probability of a categorical target variable. Despite its name, it is used for classification, not regression. It applies the sigmoid (logistic) function to a linear combination of input features, mapping the output to a probability value between 0 and 1, commonly used for binary classification.

## Industry Applications & Real-World Use Cases
Binary classification, spam detection benchmarks, and probability estimating.

## Frequently Asked Questions

### How does logistic regression differ from linear regression?
Linear regression predicts continuous numerical outcomes. Logistic regression predicts probabilities between 0 and 1 using a logistic Sigmoid function.

### What is the decision boundary in logistic regression?
A threshold probability (typically 0.5) where inputs scoring above are classified as positive, and below as negative.

## Related Terminology
- [supervised-learning](https://spidits.com/ai-glossary/supervised-learning)
- [sigmoid](https://spidits.com/ai-glossary/sigmoid)
- [linear-regression](https://spidits.com/ai-glossary/linear-regression)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*