The Difference Between Training Data vs. Test Data in Machine Learning

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Your predictions are only as good as your data. And if you aren’t using the right data, you aren’t setting your models up for success.

Something we get asked about a lot is the difference between training data and testing data. It’s so important to not only know the difference, but ensure you’re using both the right way. 

In this article, we’ll compare training data vs. test data and explain the place for each in machine learning

What is Training Data?

Machine learning uses algorithms to learn from data in datasets. They find patterns, develop understanding, make decisions, and evaluate those decisions. 

In machine learning, datasets are split into two subsets. 

The first subset is known as the training data - it’s a portion of our actual dataset that is fed into the machine learning model to discover and learn patterns. In this way, it trains our model. 

The other subset is known as the testing data. We’ll cover more on this below.

Training data is typically larger than testing data. This is because we want to feed the model with as much data as possible to find and learn meaningful patterns. Once data from our datasets are fed to a machine learning algorithm, it learns patterns from the data and makes decisions.

Algorithms enable machines to solve problems based on past observations. Kind of like learning from example, just like humans. The only difference is that machines require a lot more examples in order to be able to see patterns and learn. 

As machine learning models are exposed to more relevant training data, the more they improve over time. 

Your training data will vary depending on what type of machine learning you’re using: supervised or unsupervised. We cover the differences between these two in our blog post on machine learning vs. artificial intelligence.

To summarize: Your training data is a subset of your dataset that you use to teach a machine learning model to recognize patterns or perform your criteria. 

What is Testing Data?

Once your machine learning model is built (with your training data), you need unseen data to test your model. This data is called testing data, and you can use it to evaluate the performance and progress of your algorithms’ training and adjust or optimize it for improved results. 

Testing data has two main criteria. It should:

  • Represent the actual dataset 
  • Be large enough to generate meaningful predictions

Like we said above, this dataset needs to be new, “unseen” data. This is because your model already “knows” the training data. How it performs on new test data will let you know if it’s working accurately or if it requires more training data to perform to your specifications.

Test data provides a final, real-world check of an unseen dataset to confirm that the machine learning algorithm was trained effectively.

In data science, it’s typical to see your data split into 80% for training and 20% for testing.

Note: In supervised learning, the outcomes are removed from the actual dataset when creating the testing dataset. They are then fed into the trained model. The outcomes predicted by the trained model are compared with the actual outcomes. Depending on how the model performs on the testing dataset, we can evaluate the performance of the model.

Why Knowing the Difference is Important

The difference between training data vs. test data is clear: one trains a model, the other confirms it works (or doesn’t work) correctly with previously unseen data.

However, confusion can pop up between the similarities and differences of both. And sometimes, at Obviously AI, we’ll see some people try and use their training data to make predictions. 

This is why knowing the difference between the two is so important - you want to ensure you’re fuelling your models with the right data so you can get the best, most accurate insights. After all, those insights will feed directly into your decision-making. 

Now that we’ve covered the differences between the two, let’s dive deeper into how training and testing data work. 

How Training and Testing Data Work

Machine learning models are built off of algorithms that analyze your training dataset, classify the inputs and outputs, then analyze it again. 

Trained enough, an algorithm will essentially memorize all of the inputs and outputs in a training dataset — this becomes a problem when it needs to consider data from other sources, such as real-world customers.

The training data process is comprised of three steps:

  1. Feed - Feeding a model with data
  2. Define - The model transforms training data into text vectors (numbers that represent data features) 
  3. Test - Finally, you test your model by feeding it test data (unseen data).

When training is complete, then you’re good to use the 20% of data you saved from your actual dataset (without labeled outcomes, if leveraging supervised learning) to test the model. This is where the model is fine-tuned to make sure it works the way we want it to. 

In Obviously AI, the entire process (training and testing) is conducted in a matter of seconds, so you don’t have to worry about fine-tuning. However, we always say that it’s always good to know what’s happening behind the scenes so it’s not a black box. 

How Much Training Data do You Need

We get asked this question a lot, and the answer is: It depends.

We don't mean to be vague—this is the kind of answer you'll get from most data scientists. That's because the amount of data required depends on a few factors, such as:

  • The complexity of the problem
  • The complexity of the learning algorithm

In Obviously AI, we always say: the more data, the better. That's because the more you train your model, the smarter it will become. But so long as your data is well prepared, follows a basic data prep checklist, and is ready for machine learning, then you'll still achieve accurate results. And, with our platform, those accurate results are generated in seconds.

Summary

Good training data is the backbone of machine learning. Understanding the importance of training datasets in machine learning ensures you have the right quality and quantity of training data for training your model.

Now that you understand the difference between training data and test data and why it’s important, you can put your own dataset to work. Book a demo with our team to see how quickly a trained model is generated with your data.

Want more? Check out our blog post on the importance of having clean data for machine learning. 

If you’re looking for a deep dive on all things AI and machine learning, be sure to check out our Ultimate Guide to Machine Learning.




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