What is Logistic Regression? Binary Classification Explained

What is Logistic Regression? Binary Classification Explained

Despite its name, Logistic Regression is not used for regression (predicting continuous numbers); it is a fundamental classification algorithm. It is used to predict the probability that a given input belongs to a specific category (e.g., spam or not spam, fraud or safe). It achieves this by passing a linear equation output through a mathematical Sigmoid function, mapping any real number to a value between 0 and 1.

Sigmoid and Classification Logic

• Sigmoid Function: Formula is 1 / (1 + e^-z), mapping outputs strictly between 0 and 1.
• Probability Mapping: An output of 0.75 means there is a 75% probability of belonging to class 1.
• Decision Threshold: Usually set to 0.5; values above become class 1, below become class 0.
• Cost Function: Uses Log Loss (Cross-Entropy) to measure classification error.
• Multi-Class Classification: Extended via One-vs-Rest (OvR) or Softmax strategies.

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