Introduction

You’ve heard of deep learning, neural networks, and other machine learning methods. But what is problem framing? And how do you use it when building models? In this article, we’ll take a look at how problem framing can help you build more effective machine learning models.

What is problem framing?

Problem framing is the process of analyzing a problem to isolate the individual elements that need to be addressed to solve it. Problem framing helps determine your project's technical feasibility and provides a clear set of goals and success criteria. When considering an ML solution, effective problem framing can determine whether or not your product ultimately succeeds.

How do you frame a problem in machine learning

The problem to be solved in machine learning is usually one that can be modeled by a given equation or function.
At a high level, ML problem framing consists of two distinct steps:

• Decide if ML is the right approach to solving a problem.
• Describe your problem in ML terms.

Understand the problem

To understand the problem, perform the following tasks:

-State your goals for the product you are developing or redesigning.
-Decide if your objective is best solved with ML.
-Make sure you have the data you need to train your model.

Framing an ML problem

After verifying that your problem is best solved by ML and that you have access to the data you will need, you are ready to shape your problem in ML. You identify an ML problem by performing the following actions:

-Determine the ideal outcome and goal of the model.
-Determine the output of the model.
-Define success metrics.

Choose a Dataset

Datasets are collections of data that have been carefully chosen for their properties as well as for the task at hand. Data sets will vary based on the problem you want to solve, but they typically contain some common information that will help you model the behavior you seek to describe.

Choose the problem

There are many different types of problems that can be modeled by machine Learning algorithms, but two of the most popular are image recognition and text recognition. Image recognition is used in tasks such as recognizing objects in images or videos, while text recognition is used in tasks such as understanding natural language expressions.

the Problem Terms

Now that you have a good data set and a problem that needs solving, it’s time to define what those terms mean for your algorithm. Here are some examples:

• Data: The information your machine learning algorithm needs in order to train its models
• Problem: The specific task or question you want your machine learning algorithm to solve
• Solution: How your machine learning algorithm will create a solution for your problem

Conclusion

In order to frame a problem in machine learning, you first need to decide the problem. Next, you need to choose a data set and solve the problem with that data set. Finally, you need to define the problem terms in order to be able to solve the problem correctly.