Editing Logistic regression (section)

=== Supervised machine learning ===
Logistic regression is a [[supervised machine learning]] algorithm widely used for [[binary classification]] tasks, such as identifying whether an email is spam or not and diagnosing diseases by assessing the presence or absence of specific conditions based on patient test results. This approach utilizes the logistic (or sigmoid) function to transform a linear combination of input features into a probability value ranging between 0 and 1. This probability indicates the likelihood that a given input corresponds to one of two predefined categories. The essential mechanism of logistic regression is grounded in the logistic function's ability to model the probability of binary outcomes accurately. With its distinctive S-shaped curve, the logistic function effectively maps any real-valued number to a value within the 0 to 1 interval. This feature renders it particularly suitable for binary classification tasks, such as sorting emails into "spam" or "not spam". By calculating the probability that the dependent variable will be categorized into a specific group, logistic regression provides a probabilistic framework that supports informed decision-making.<ref>{{Cite web |title=Logistic Regression |url=https://www.mastersindatascience.org/learning/machine-learning-algorithms/logistic-regression/ |access-date=2024-03-16 |website=CORP-MIDS1 (MDS) |language=en-US}}</ref>