What kind of prediction organizes cases based on the input's relationship to the target variable?

The correct choice for the type of prediction that organizes cases based on the input's relationship to the target variable is classification. In a classification context, the goal is to assign cases or observations into predefined categories or groups based on their characteristics and how they relate to the target variable. This approach is particularly useful when the target variable is categorical, enabling the model to learn from the data and make predictions about which category new cases belong.

For example, if the target variable is whether an email is "spam" or "not spam," a classification model will analyze the features of the emails (such as keywords, sender information, etc.) to classify new emails into one of those two categories. This process involves identifying patterns in the input data that correlate with specific outcomes.

The other options, while related to prediction, are distinct from classification: ranking typically involves ordering cases based on their predicted probability or score, estimation refers to predicting continuous values rather than categories, and decision involves selecting among multiple alternatives but does not inherently organize cases into categories like classification does.