What type of regression analysis provides a classification probability between 0 and 1?

Logistic Regression is designed specifically to handle classification problems and provides probabilities that lie within the range of 0 to 1. This feature is crucial for tasks where the outcome is categorical, such as deciding whether an email is spam or not, or if a customer will buy a product.

In logistic regression, the output is transformed using the logistic function (or sigmoid function), which maps any real-valued number into the (0, 1) interval. This probability can then be interpreted as the likelihood that a given data point belongs to a particular class. For instance, an output of 0.7 suggests a 70% probability of belonging to the target class.

Other types of regression listed do not offer this probability framework suitable for classification tasks. Linear Regression outputs continuous values that can exceed the bounds of 0 and 1, making it inappropriate for classification. Polynomial Regression, while also a form of linear regression applicable to more complex relationships, operates on the same principle of outputting continuous values. Ridge Regression is primarily a method to address multicollinearity in linear regression by adding a penalty term, while still producing continuous outputs, not probabilities for classification. Thus, the design of Logistic Regression is precisely what enables it to deliver classification probabilities