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?
Click on the arrows to vote for the correct answer
A. B. C. D.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?
The modeling approach that best fits the problem of identifying the make and model of cars passing a camera on state highways is Multi-Class Classification.
Multi-class classification is a type of machine learning problem where the goal is to classify instances into one of three or more classes or categories. In this scenario, the make and model of the cars passing the camera on the state highways would be the classes that the machine learning model needs to classify the instances into.
Binary classification, on the other hand, is used when there are only two classes or categories, for example, whether a particular email is spam or not.
Simulation-based Reinforcement Learning is a type of machine learning where the agent interacts with an environment, learning by trial and error to achieve a goal. This approach is typically used in complex scenarios where a traditional algorithmic approach would be difficult to implement.
The heuristic approach is a problem-solving strategy that involves using a practical, common-sense method to find a solution. This approach is often used in situations where there is no mathematical or logical algorithm that can be applied.
In conclusion, Multi-Class Classification is the most suitable modeling approach to solve the problem of identifying the make and model of cars passing a camera on state highways.