Not Using Accuracy in Machine Learning

Not Using Accuracy in Machine Learning | Machine learning is all the raves nowadays. With technology growing smarter and more convenient these days, it’s no wonder there has been more talk about machine learning in more industries than one. Now, when you think of machine learning, what comes to mind first? You might say that accuracy is part of it. Well, yes and no. 

Here’s how it works: People feed any type of data into a machine learning device. Next, machine learning takes into consideration that data. Then, machine learning sees said data as fact, thus artificial intelligence (AI) steps in and declares it fact. While accuracy seems to fit into this process, you’ll also have to take into account the degree of predictions that your model can guess correctly.

But… can accuracy really be trusted in machine learning? This post will delve into accuracy in machine learning, and why you may want to think twice before fully relying on it.

Define Accuracy

“At first glance, accuracy refers to the percentage of correct predictions for whatever data you’re testing,” says Jason Marano, a tech writer at Writinity and Draftbeyond. “Accuracy is normally calculated when you divide the number of correct predictions by the number of total predictions made.

However, when it comes to machine learning, accuracy can either help it or harm it. But in many cases, you should still consider whether or not accuracy is needed for your machine learning model.”

Risk Of Bias

Machine learning is meant to “learn” from what it’s being fed. Whatever information is being fed into it, it’ll perceive as knowledge – an expectation that it’s being trained to perform. Now, how this is done depends on the type of model that you’re using:

  • A linear regression model is made to catch any linear relationships between the information that’s being fed to machine learning.
  • A pre-imposed structure model thoroughly studies what machine learning should be… well, learning.

However, having a pre-imposed structure can limit the model’s ability to learn from examples. This can cause a biased operation when it comes to machine learning. Bias can come in one of two forms: underfitting and overfitting.


“Underfitting” refers to models with high bias paying little attention to the presented data. This also happens whenever you try to teach machine learning to do something without presenting all the right information. This hinders other features in trying to train your model. 


On the other hand, “overfitting” refers to when you try to train machine learning to learn too much from the data. In other words, you’re teaching it the “noise” of the data, not the necessary part of it. It’s like leaving junk in a bedroom while leaving necessary things like a bed and a dresser outside the room. 

Overfitting will cause exaggerated trends in your model, rather than reveal the true trends. Plus, this can cause overgeneralizing basic things while trivializing more valuable data. 

So, how do we solve this problem? How can we not use accuracy in machine learning? The truth is, you’ll need a baseline in order for your model to work, as you work with machine learning. 

What is a Baseline?

“Having a baseline is essential when looking at the algorithms produced by machine learning,” says Lawrence Miles, a business blogger at Researchpapersuk. “A baseline is what you use when you compare algorithms. When there’s a comparison basis, you’ll learn from whether something is good or bad.”

Creating a Baseline

Since a baseline is needed in that case, you’ll need to do so as follows: 

  • Choose a class that has the most observations in your data set. 
  • Next, “predict” everything as the chosen class. This shows you what would be your accuracy without using a model.
  • Your class-balanced data sets should have a baseline of more or less than 50%. No matter what, calculate which data is more presentable when observing them.
  • If your accuracy isn’t very different from your baseline, collect some more data, tweak the data, or change the algorithms.


As you can see, accuracy might or might not work in your favor when working with machine learning. That’s why it’s important to look at your data and determine if whether or not accuracy is needed. Keep in mind: People use different models, meaning that there’s no one-size-fits-all approach to machine learning. 

Jenny Williams is a writer and editor at Coursework Writing Service and Assignment Help. She is also a contributing writer for As a content writer, she writes articles about coding, tech trends, and machine learning.

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