Overview
Logistic regression is a powerful statistical tool used for predicting binary outcomes based on one or more predictor variables. It transforms the predicted probabilities into log-odds using the logit function, allowing for easier interpretation of the relationship between the predictors and the out...
Key Terms
Example: Determining if an email is spam or not.
Example: An odds ratio of 2 means the event is twice as likely to occur.
Example: Logit(p) = log(p/(1-p)) where p is the probability.
Example: Used to find the best-fitting logistic regression model.
Example: It shows true positives, false positives, true negatives, and false negatives.
Example: Used to visualize the trade-off between sensitivity and specificity.
Related Topics
Linear Regression
A method for modeling the relationship between a dependent variable and one or more independent variables.
beginnerDecision Trees
A model that uses a tree-like graph of decisions and their possible consequences.
intermediateSupport Vector Machines
A supervised learning model that analyzes data for classification and regression analysis.
advancedKey Concepts
Binary ClassificationOdds RatioLogit FunctionMaximum Likelihood Estimation