Training Data vs. Validation Data vs. Test Data for ML Algorithms
Learn the difference between these ML datasets
Machine learning lets companies turn oodles of data into predictions that can help the business. These predictive machine learning algorithms offer a lot of profit potential.
However, effective machine learning (ML) algorithms require quality training and testing data — and often lots of it — to make accurate predictions. Different datasets serve different purposes in preparing an algorithm to make predictions and decisions based on real-world data.
In this article, we’ll compare training data vs. test data vs. validation data and explain the place for each in machine learning. While all three are typically split from one large dataset, each one typically has its own distinct use in ML modeling. Let’s start with a high-level definition of each term:
Training data. This type of data builds up the machine learning algorithm. The data scientist feeds the algorithm input data, which corresponds to an expected output. The model evaluates the data repeatedly to learn more about the data’s behavior and then adjusts itself to serve its intended purpose.
Validation data. During training, validation data infuses new data into the model that it hasn’t evaluated before. Validation data provides the first test against unseen data, allowing data scientists to evaluate how well the model makes predictions based on the new data. Not all data scientists use validation data, but it can provide some helpful information to optimize hyperparameters, which 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 ML 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.
Let’s further explore the differences between training data, validation data and testing data, and how to properly train an ML algorithm.
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 ML algorithm can assess training data and validation data at the same time.
Validation data is an entirely separate segment of data, though a data scientist might carve out part of the training dataset for validation — as long as the datasets are kept separate throughout the entirety of training and testing.
For example, let’s say an ML algorithm is supposed to analyze a picture of a vertebrate 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 in that task. This is a check against an entirely different dataset than the one it was trained on.
Based on the accuracy of the predictions after the validation stage, data scientists can adjust hyperparameters such as learning rate, input features and hidden layers. These adjustments prevent overfitting, in which the algorithm can make 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, when you see good predictions on both the training datasets and validation datasets, 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 practical differences between validation data and testing data. If you opt to 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.
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 ML algorithms, validation data typically helps tune the algorithm and testing data provides the final assessment.
Craft better ML algorithms
Now that you understand the difference between training data, validation data and testing data, you can begin to effectively train ML algorithms. But it’s easier said than done.
In some ways, an ML algorithm is only as good as its training data — as the saying goes, “garbage in, garbage out." Effective ML training data is built upon three key components:
Quantity. A robust ML algorithm needs lots of training data to properly learn how 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 to ensure the algorithm works as expected.
Quality. Volume alone will only take your ML algorithm so far. The quality of the data is just as important. This means collecting real-world data, such as voice utterances, images, videos, documents, sounds and other forms of input on which your algorithm might rely. Real-world data is critical, as it takes a form that most closely mimics how an application will receive user input, and therefore gives your application the best chance of succeeding in its mission. 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.
Diversity. The third piece of the pie is diversity of data, which is essential to eliminate the dreaded problem of AI bias, where the application works better for a certain segment of the population than others. With AI bias, the ML algorithm delivers results that can be seen as prejudiced 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.
Depending on the type of ML approach and the phase of the buildout, labels or tags might be another essential component to data collection. In supervised learning approaches, clearly tagged data and direct feedback ensures 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.
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, biometrics and more.
You no longer have to choose between time to market and effective algorithm training. Applause can help you train and test an algorithm with the types of data you need, on your target devices. Contact us today.