Which type of regression is characterized by binning of continuous inputs?

The type of regression that is characterized by binning of continuous inputs is DMine regression. DMine regression is specifically designed to handle large datasets with high dimensionality and involves the process of categorizing continuous variables into bins. This binning technique allows for the transformation of these continuous inputs into categorical predictors, which enhances the interpretability of the model and can help capture non-linear relationships between independent and dependent variables.

In contrast, logistic regression focuses on modeling binary outcomes rather than binning continuous inputs. Linear regression analyzes the relationship between independent and dependent variables in a straight line without the need for binning. Polynomial regression allows for fitting curves by introducing polynomial terms to the predictors but does not inherently involve the binning of continuous inputs. Each of these regression types has different methodologies and applications, but DMine regression explicitly involves the binning process.