Training Data, Validation Data and Test Data in Machine Learning (ML)

Artificial intelligence and machine learning lets companies turn oodles of data into predictions that can help the business. Predictive algorithms offer a lot of profit potential. Generative AI also provides a new, user-friendly way for customers to interact dynamically with your brand.

Quality training and testing data fuel machine learning (ML) algorithms and large language models (LLMs) — and they often require lots of it to make accurate predictions. Different datasets serve different purposes. Preparing an algorithm to make predictions means collecting lots of quality data. And, as brands increasingly trust AI in decision making, it’s incredibly important to make sure the data is accurate and reduces any biases that can lead to harmful conclusions.

Ebooks

In this blog, we’ll compare training data vs. test data vs. validation data and explain the place for each. While all three are typically split from one large dataset, each one typically has its own distinct use in AI modeling. Let’s start with a high-level definition of each term.

What are training data, validation data and test data?

AI models can only provide helpful responses if they have high-quality data to access. AI models typically rely on these types of data:

Training data

The ML algorithm collects training data, essentially input data, then interprets it to inform its conclusions. The model evaluates the data repeatedly to learn more about the data’s behavior and then adjusts itself to serve its intended purpose. Training datasets originate from numerous sources depending on the purpose of the algorithm. Some training data sources include publicly accessible datasets, web-scraped data, user-generated content, proprietary company data, crowdsourced contributions and sometimes synthetic datasets created specifically for training.

Validation data

During training, validation data infuses new data into the model. Validation data provides the first test against unseen data. This type of data allows the team to evaluate how well the model makes predictions based on new information. Validation data can provide helpful information to optimize hyperparameters. These hyperparameters influence how the model assesses data.

Test data

After the model is built, testing data once again validates that it can make accurate predictions. If training and validation data include labels to monitor performance metrics of the model, the testing data should be unlabeled. Test data provides a final, real-world check of an unseen dataset to confirm that the ML algorithm was trained effectively.

While each of these three datasets has its place in creating and training models, it’s easy to see some overlap between them. The difference between training data vs. test data is clear: one trains a model, the other confirms it works correctly, but confusion can pop up between the functional similarities and differences of other types of datasets.

Training data vs. validation data

ML algorithms require training data to achieve an objective. The algorithm will analyze this 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.

Here is where validation data is useful. Validation data provides an initial check that the model can return useful predictions in a real-world setting, which training data cannot do. The algorithm can assess training data and validation data at the same time.

Validation data is an entirely separate segment of data. Though part of the training dataset might be carved out for validation — as long as the datasets are kept separate throughout the entirety of training and testing.

For example, let’s say an algorithm will analyze a vertebrate image and provide its scientific classification. The training dataset would include lots of pictures of mammals, but not all pictures of all mammals, let alone all pictures of all vertebrates. So, when the validation data provides a picture of a squirrel, an animal the model hasn’t seen before, the data scientist can assess how well the algorithm performs. This is a check against an entirely different dataset.

Webinar

Data scientists can adjust hyperparameters, such as learning rate, input features and hidden layers, based on the accuracy. These adjustments prevent overfitting, in which the algorithm makes excellent determinations on the training data, but can’t effectively adjust predictions for additional data. The opposite problem, underfitting, occurs when the model isn’t complex enough to make accurate predictions against either training data or new data.

In short, the model must make good predictions on both the training datasets and validation datasets. Then, you can have confidence that the algorithm works as intended on new data, not just a small subset of data.

Validation data vs. testing data

Not all data scientists rely on both validation data and testing data. To some degree, both datasets serve the same purpose: make sure the model works on real data.

However, there are some differences between validation data and testing data. If you include a separate stage for validation data analysis, this dataset is typically labeled so the data scientist can collect metrics that they can use to better train the model. In this sense, validation data occurs as part of the model training process. Conversely, the model acts as a black box when you run testing data through it. Thus, validation data tunes the model, whereas testing data simply confirms that it works.

Webinar

Revisiting the vertebrate classification example, validation data might test the model with an unfamiliar animal like a squirrel. This ensures it adjusts predictions based on unseen inputs. Testing data, however, evaluates the model’s overall ability to generalize across entirely new and unlabeled datasets. While validation data might confirm that the model can identify a labeled photo of a squirrel as a vertebrate, testing data would assess if the model can classify other novel vertebrates, such as amphibians or reptiles, which it did not encounter during training or validation.

There is some semantic ambiguity between validation data and testing data. Some organizations call testing datasets “validation datasets.” Ultimately, if there are three datasets to tune and check algorithms, validation data typically helps tune the algorithm and testing data provides the final assessment.

Craft better algorithms

Now that you understand the difference between training data, validation data and testing data, you can begin to effectively train ML algorithms. But the explosion of use around generative AI makes this task harder than ever.

Generative AI can enhance training datasets by creating synthetic data. This approach supplements real-world data to help fill data gaps, reduce bias and provide scenarios that mirror real-world complexities. By leveraging synthetic data, organizations can ensure their models can better handle a wide range of inputs while maintaining accuracy and fairness. 

In some ways, an algorithm is only as good as its training data—as the saying goes, “garbage in, garbage out.” Effective training data is built upon three key components:

Quantity

A robust algorithm needs lots of training data to learn to interact with users and behave within the application. Think about humans; we must take in a lot of information before we can call ourselves experts at anything. It’s no different for software. Plan to use a lot of training, validation and test data. These data types ensure the algorithm accounts for all expected uses and scenarios. And expect to add plenty more data over time. Consider how gGenerative AI must adapt its outputs for a general purpose audience. That requires lots of data suited to those different purposes, and ideally as up-to-date as possible.

Quality

Volume alone will only take the algorithm so far. Data quality is just as important. This means collecting real-world data. Multi-modal data, such as voice utterances, images, videos, documents, sounds, and other forms of input, also train the model. Real-world data is critical, as it mimics how an application will receive user input. Thus, real-world data gives your application the best chance of success. For example, ML algorithms that rely on visual and/or sonic inputs should source training data from the same or similar hardware and environmental conditions expected once deployed. Remember that the quality of your training data, validation data and testing data influence real-world outcomes, such as approval recommendations on loan applications, so the task of sourcing and fostering quality data should not be taken lightly.

Diversity

The third piece of the pie is diversity of data, which is essential to eliminate the dreaded problem of AI bias. Bias arises when algorithms are fed training data, validation data or testing data that leads to outcomes drawn along discriminatory lines. In these cases, the ML algorithm delivers results that can be seen as prejudiced toward or against a certain gender, race, age group, language or culture, depending on how it manifests. Make sure the algorithm has “seen it all” before you release the application and rely on it to perform on its own. Biased ML algorithms should not speak for your brand. Train algorithms with artifacts comprising an equal and wide-ranging variety of inputs. When this data is lacking, make extra efforts to source it with real-world customers — efforts to backfill this data synthetically are as limited as your algorithms are.

Labels or tags might be an essential component to data collection, depending on the approach. In supervised learning approaches, clearly tagged data and direct feedback ensure that the algorithm can self-learn. This increases the work involved in training and testing algorithms, and it requires accuracy in the face of tedium and often tight deadlines. However, this effort will take you that much further toward a successful implementation.

Turn to a trusted partner with AI expertise

Applause helps companies source high-quantity and high-quality training and testing data from all over the world. Our diverse community of digital experts provides the right context for the algorithm in your application and helps reduce AI bias. Applause can source training, validation and testing data in whatever forms you need: text, images, video, speech, handwriting and more.

Generative AI represents a paradigm shift in artificial intelligence. Gen AI can empower organizations to provide engaging, hyper-personalized experiences while maintaining trust and efficiency. Applause has trained and tested some of the largest LLMs and generative AI programs worldwide. Our fully managed solution, powered by our million-plus global community, covers every aspect of your Gen AI training and testing program. We provide immediate access to the datasets, testers and expertise you need. All of this helps you build a program that delivers reliable AI apps, devices and experiences on-schedule and on-budget.

With Applause, you no longer have to choose between time to market and effective AI training. Our solutions across generative AI testing, as well as AI training and testing ensure safety, reliability and inclusivity for your digital products. Contact us today to learn how we can help you stay ahead in AI innovation.

Ebook