You work as a machine learning specialist for a state highway administration department. Your department is trying to use machine learning to help determine the make and model of cars as they pass a camera on the state highways. You need to build a machine learning model to accomplish this problem.
Which modeling approach best fits your problem?

Answer options:

A.Multi-Class Classification
B.Simulation-based Reinforcement Learning
C.Binary Classification
D.Heuristic Approach

Answer: A
Option A is correct. Multi-Class Classification is used when your model needs to choose from a finite set of outcomes, such as this car make and model classification image recognition problem.
Option B is incorrect. Simulation-Based Reinforcement Learning is used in problems where your model needs to learn through trial and error. An image recognition problem with a finite set of outcomes is better suited to a multi-class classification model.
Option C is incorrect. Binary Classification is the approach you use when you are trying to predict a binary outcome. This strategy determination problem would not fit a binary classification model since you have a finite set from which to choose that is greater than 2.
Option D is incorrect. The Heuristic Approach is used when a machine learning approach is not necessary. An example is the rate of acceleration of a particle through space. There are well known formulas for speed, inertia, and friction that can solve a problem such as this.
Reference:
Please see the Amazon SageMaker developer guide titled Linear Learner Algorithm, the Amazon SageMaker developer guide titled Reinforcement Learningwith Amazon SageMaker RL,the Amazon Machine Learning developer guide titled Multiclass Classification, and the article titled What is the difference between a machine learning algorithm and a heuristic, and when to use each?