Descriptive modeling machine learning

1. What is descriptive modeling in machine learning?
2. What is descriptive data modeling?
3. What is the difference between descriptive Modelling and predictive Modelling in machine learning?
4. What is the difference between descriptive and prescriptive modeling?

What is descriptive modeling in machine learning?

A descriptive model describes a system or other entity and its relationship to its environment. It is generally used to help specify and/or understand what the system is, what it does, and how it does it. A geometric model or spatial model is a descriptive model that represents geometric and/or spatial relationships.

What is descriptive data modeling?

Descriptive modeling is a mathematical process that describes real-world events and the relationships between factors responsible for them. The process is used by consumer-driven organizations to help them target their marketing and advertising efforts.

What is the difference between descriptive Modelling and predictive Modelling in machine learning?

A descriptive model will exploit the past data that are stored in databases and provide you with the accurate report. In a Predictive model, it identifies patterns found in past and transactional data to find risks and future outcomes.

What is the difference between descriptive and prescriptive modeling?

Models that are primarily used for understanding, predicting and communicating are referred to as descriptive models, whereas models mainly used for implementation are called prescriptive models. This contribution focuses on teaching both the common and the distinguishing aspects of the two model categories.