Logistic Regression
Logistic regression is a statistical method used to predict the outcome of a binary event—one that has two possible results, like yes/no or pass/fail. It works by analyzing the relationship between one or more input variables (like age or income) and the probability of a certain outcome occurring. Instead of predicting a direct numerical value, logistic regression estimates the likelihood of an event happening, using an S-shaped curve to ensure the probabilities remain between 0 and 1. This makes it useful in fields like healthcare, finance, and marketing, where understanding risks and probabilities is essential.
Additional Insights
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Logistic regression is a statistical method used to predict the likelihood of a particular outcome based on one or more input variables. Unlike standard regression, which can predict a range of values, logistic regression outputs probabilities that range from 0 to 1. It’s commonly used in situations where the outcome is binary, such as determining whether an email is spam or not. By analyzing the relationship between the input factors and the outcome, logistic regression helps us understand how different features influence the probability of an event occurring.