What type of data is logistic regression not suitable for?

Logistic regression is a statistical method used for binary classification tasks, particularly when the outcome or dependent variable is categorical, such as yes/no, success/failure, or 0/1. While logistic regression can technically handle some continuous features, it is best suited for scenarios where the response variable is not continuous but categorical.

When we consider the nature of the data that logistic regression is designed for, categorical data (including binary and multinomial categories) is appropriate. Binary data is a fundamental use case for logistic regression, not a limitation. Multivariate data can also be used, as logistic regression can incorporate multiple independent variables. Continuous data, on the other hand, while it can be input into a logistic regression model as predictors, does not serve as the model's output.

Thus, logistic regression isn’t suitable for situations where the target variable is continuous, because the model is fundamentally built to predict categories rather than numerical values. The correct answer reflects the model's purpose and design, focusing on its application in classification tasks rather than regression tasks that deal with continuous outputs.