Learn how to use logistic regression to predict the probability of a binary outcome based on explanatory variables, and understand the assumptions and interpretations of the model.

Logistic regression is another type of generalized linear models that are used to model the probability of a binary outcome, such as success or failure, yes or no, or presence or absence.

Logistic regression. Linear regression. Outcome variable . Models binary outcome variables. Models continuous outcome variables. Regression line. Fits a non-linear S-curve using the sigmoid function .

Accuracy, Precision, and F1 Score. Data practitioners can use the numbers derived from a confusion matrix to calculate their logistic regression models’ accuracy, recall, and F1 score.

The logistic regression model is used in place of the linear regression model when the dependent variable is primarily dichotomous. Multicollinearity occurs when the independent variables in a ...

Course Topics"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count ...