Which of the following are considered primary types of predictive models?

The identification of classification models and regression models as primary types of predictive models is accurate because these two methodologies serve as foundational techniques in predictive analytics.

Classification models are designed to predict categorical outcomes. They distinguish between different classes or labels based on input features, using algorithms like logistic regression, decision trees, or support vector machines. This type of modeling is essential in scenarios where the outcome variable is categorical, such as spam detection in emails or customer churn prediction.

On the other hand, regression models are used to predict continuous outcomes. They estimate relationships among variables, where the outcome is a numerical value. Common techniques include linear regression, polynomial regression, and neural networks. Regression models are crucial in fields where quantifying relationships is necessary, such as forecasting sales or estimating property values.

The other options introduce terms that don't represent the primary categories of predictive modeling. Clustering models, while important in data analysis for grouping similar data points, do not fall under the predictive model category that specifically focuses on outcomes. Survival models are specialized for time-to-event analyses and predictive maintenance models are applications rather than broad categories. Lastly, imaginary models do not exist in the context of predictive analytics, making them non-applicable.