Logistic Regression Explained

Logistic regression is used when the output has only two possible outcomes, like yes or no, or true or false, despite having "regression" in its name.

Instead of predicting a straight-line value, it calculates the probability that something belongs to a particular class, then converts that probability into a final decision.

For example, a bank might use logistic regression to decide whether a loan applicant is likely to default, based on income, credit score, and past repayment history.

• The output is always a probability between 0 and 1.
• A threshold, usually 0.5, decides the final class.
• It works best when the relationship between input and output is fairly simple.

Logistic regression remains popular because it is fast, easy to interpret, and gives reliable results on straightforward classification problems.