Training data is used to teach machine learning (ML) algorithms how to make accurate predictions. Procuring the kind of data that can be used for this training has typically been a resource and time-consuming process. Here we’ll explain what training data consists of, how to get high-quality training data and how to determine how much you’ll need.

What is training data?

The data used to train an ML model is called “training data.” The type of data you need depends on the type of ML project and the variety of problems the algorithms will need to solve.

Supervised Learning

Supervised learning is when humans choose the data features in their models. The training data is labeled (enriched or annotated) to teach your artificial intelligence (AI) model what it is looking at, so it can then perform tasks like object detection and tracking.

Unsupervised Learning

Unsupervised learning is when unlabeled data (not enriched or annotated) is presented to the AI, which looks for patterns such as inferences or clustered data points. Unlabeled data can also be called raw data.

Hybrid Learning

This allows you to train your machine learning system using a combination of supervised and unsupervised learning. In other words, you train with both labeled and unlabeled data.

Reinforcement learning

Reinforcement Learning (RL) is when you train a model to produce outcomes with the intent of maximizing cumulative reward.

What are the forms of training data?

Training data comes in as many forms as there are applications for a machine learning algorithm.

Synthetic training datasets may contain:

  • Text (words and numbers)
  • Images
  • Video
  • Audio

Training data can also come in many formats:

  • Spreadsheets
  • PDF
  • HTML
  • JSON
  • And more

What is labeled data?

Data is labeled to show the “target” or endpoint that the machine learning model is supposed to predict. Data labeling goes by many names.

  • Tagging
  • Annotation
  • Moderation
  • Transcription
  • Processing
  • Marking

Data labeling entails adding details to your dataset that train the algorithm. The algorithm learns to identify features and patterns that the labeled data has deliberately highlighted so that the same features and patterns can be identified in unlabeled data.

For example, a machine learning model is taught using supervised learning to identify vehicular and pedestrian traffic congestion in a smart city based on time of day. One outcome of your model could be identifying “peak traffic times”, which would allow you to tweak traffic signal patterns to reduce congestion at intersections.

Properly labeled data can also be the “ground truth” that allows you to build an evolving, functioning machine learning formula. Ground truth refers to the accuracy or verisimilitude of machine learning algorithms’ results relative to the real world. The term comes from meteorology, where “ground truth” is the information at the scene of the weather event that’s compared to forecast models to ascertain their correctness.

The model’s accuracy is affected by how each label is scored or weighted and how the test cases are managed. Having labelers with experience in the niche in which your business operates will improve your results’ quality. The human influence on data is known as “the human in the loop.” In terms of data labeling, the humans in the loop are the people who gather and prepare the data for use.

What do the humans in the loop do to training data?


Gathering the data is when you gain access to the raw data and select the details that would be good predictors for your machine learning model to formulate its outcomes. The grade and amount of data that’s gathered impacts how good the model can be.


Preparing data means loading it into an appropriate place and prepping it for machine learning training.


Labeling data isn’t something that’s just done once. With every simulation, you’ll discover opportunities to refine your model. That makes the humans in the loop indispensable because they prepare and gather the data and because their professional experience impacts how well the data is prepared and gathered.

How is training data used in machine learning?

An AI algorithm isn’t pre-fabricated, and its predictions aren’t predetermined. The finished product is refined through training. How well your data is labeled will determine the accuracy of your algorithm’s predictions and outcomes and how much time you dedicate to refining the training data. If you train your data using 100 simulations, you won’t get as good a result as if you ran it through 10,000 simulations.

For example, an agricultural model can be used to determine the best time to water crops. Labels such as time of day, time of year, temperature, precipitation, the rate of absorption, evaporation, temperature, and the radiant heat of the sun can be used to predict the ideal amount of water to use while minimizing damage and maximizing conservation. You can see that it would behoove you to run more simulations given the number of variables and the desired result’s complexity.

Updating your training data is very important. As new information is gathered from the real world, your initial training data will provide less precise ground truth, leading your AI model to produce outcomes and predictions that are inaccurate, less useful, and potentially harmful. By continuously updating your training data as new information is learned, you will increase the likelihood of having accurate results.

What are some examples of AI and the training data it requires?

  • Sentiment analysis contains sentences, reviews, Tweets, etc., with labels indicating if they’re positive or negative.
  • Image recognition contains data that’s images labeled with information about the image.
  • Spam detection contains emails, texts, and comments labeled with information indicating whether the message is spam.
  • Text categorization contains sentences, while the labels contain information about the contents of the text, such as what they’re about.
  • Bounding box annotation is one way to provide multiple labels for individual pieces of data. This is the sort of labeling you may find if you’re training a machine to categorize images using different labels such as type, color, etc.

What’s the difference between training data and testing data?

  • Training data is for teaching your algorithm to make accurate predictions. At 70 to 80%, training data is the largest dataset you’ll have.
  • Testing data is what’s used by the AI system to produce predictions and outcomes.
  • Validation data is for assessing the quality of the predictions made by the algorithm.

Here’s how that would look in the case of our crop watering example.

  • Training data would include what the humans in the loop have determined how crops behave when they’re watered a particular amount at a particular time of day at a particular time of year given known weather patterns.
  • Testing data would be all the permutations of these variables. Like, what your algorithm predicts would happen within twenty minutes of your crop being watered at 8 am when it’s 100°F on an overcast day.
  • Validation data would determine the accuracy of the algorithm’s outcomes, which in this case would be real-world data of what happens in the scenario modeled by the algorithm.

You would, throughout this process, refine your training data using more accurate validation data while also potentially tweaking your algorithm to accommodate more variables or considerations.

How can I get training data?

Your algorithm or model’s intended use will determine what type of data you need and where you’ll acquire it.

For example, it takes a lot of text or audio data if you intend to use natural language processing (NLP) to teach an algorithm to read, understand, and discover meaning in language. Similarly, it takes a lot of labeled images and videos for a human vision project, which entails teaching an algorithm how to identify and gather information about things that are visible to the human eye.

There are many ways to get such data.

  • Crowdsource data.
  • In-house data.
  • A data labeling service that labels data you already have.
  • Buying already labeled data that’s used as training data.
  • Many commercial tools have auto-labeling features, but they’re not accurate enough without a human in the loop.
  • Open datasets: data that can be freely used, re-used, and redistributed provided by companies, government agencies, or academic institutions.
  • Scraping Web Data: So long as you’re using online sources such as government websites and social media platforms, you’ll be within the purview of fair use policy. Although, if you’re data scraping for commercial purposes, you’ll want to read the Terms of Service of your sources first to make sure everything is copacetic. You could also receive clarification from the website as well.

Some of these methods have downsides.

  • In-house data can be expensive and take a long time to make.
  • Outsourcing is highly dependent on the quality of the communication with your provider. The poorer the communication, the poorer your data may potentially be.
  • Crowdsourcing can be expensive because the quality is determined by consensus, which is when multiple parties agree on the quality of the data at once.

Why is it difficult to determine the amount of training data you’ll need?

Estimating the size of the training dataset requires considering several factors.

  • Complexity: Your model will need training data for every aspect it’s intended to account for. An algorithm meant to identify a dog’s breed will require more data points than an algorithm meant to identify the type of pet an animal is.
  • Training Method: The more complex your algorithm is, the more time and data it takes to train it. Structured learning works for some algorithms, but it has limited bandwidth for the amount of data it can handle. Algorithms using unsupervised learning can be trained without structure or predetermined bounds, which requires more data but allows that data to improve the algorithm more.
  • Labeling Needs: How you label the data and how much will also impact the size of the dataset. If you’re using sentiment analysis to identify the text, you could have one piece of labeling for a sentence or twenty.
  • Error Tolerance: Depending on the stakes of your model’s outcome, you may need more training data to increase your model’s efficacy. Identifying dog breeds is not as high stakes as identifying ER patients at risk of respiratory failure.
  • Diversity of Input: Models can get their input from one source or get them from many. A model meant to determine audience sentiment can focus only on Twitter, for example, or Twitter plus nine other social media platforms.
  • An Iterative Approach: Just start working on your algorithm using the training data you have and add more as need be. You’ll see what your needs are as your model becomes more refined.

How do I calculate my data needs?

Rule of 10: Just multiply the degrees of freedom your model needs by ten. This simple method is meant to accommodate the variability that each data point brings to your model.

Learning Curves: Make a graph using your existing training data to discover the correlation between the dataset’s size and the model’s accuracy after each simulation. Use the resulting correlation to determine at which point adding more data no longer brings benefit.

What determines the quality of training data?

Whether your workers are in-house, crowdsourced, or outsourced, there are three main things you can use to estimate the quality of the training data you’ll receive.

People: The selection, development, and management of the humans in the loop.

Process: How the humans in the loop do their work. This includes onboarding, quality control, task management, internal communication, etc.

Tools: The technology used by the humans in the loop to access their work, manage their tools, communicate, and maximize the amount of data that’s trained and its quality.

The experience and training of the humans in the loop are paramount to success. A skill assessment to gauge the worker’s aptitude is advised. Some training may also be necessary given the potential demands of training data. One of the shortcomings of crowdsourcing data is that the workers can change daily, making it hard to gain momentum when working on the project.

You’ll want the labeling process to be scalable, have strict quality controls, and be clearly outlined for precise project management. Since the facts on the ground used for data validation can quickly change, and you’ll need to run many simulations to refine your algorithm, you’ll want your humans in the loop to be good communicators and collaborators.

If you’re going to be handing off training data produced in-house to be outsourced, you’ll want to work with someone who can adopt your pre-existing processes and modify them to fit their team if necessary. Ideally, you’ll also want a partner who can also design a strategy from scratch.

Whether you’re crowdsourcing or outsourcing, you’ll want your humans in the loop to be using the most current technology and for the technology to be the same as or at least compatible with your technology. You’ll want to be flexible, so it’s best not to use proprietary data training tools. However, if you do, make sure their design is intuitive and easy to learn.

What are the attributes of quality data?

  • Uniformity: The data points are labeled in equal amounts, and the labels come from comparable sources.
  • Consistency: All data points are the same in type.
  • Comprehensiveness: The training data is robust enough to account for every outcome, including outliers.
  • Relevancy: The training data is an accurate representation of the demographics of the people who will use the model.

What are some training data best practices?

Coverage Planning: Know what your intended types of labels are, how you plan to distribute those labels, and evaluate those labels’ quality.

Check for Structured and Unstructured Data: Know whether or not you’re going to be using structured data (labeled data), unstructured data (unlabeled data), or a mix of the two. Unstructured data is original, unlabeled content that’s more comprehensive.

Feature Extraction: By being more comprehensive in content, unstructured data contains significant superfluous data. Separate the wheat from the chaff so that you only have what you need.

Mitigate Bias: Bias is the outcome of data that doesn’t accurately reflect the conditions your model will be used in. Examples include gender bias, racial bias, observer bias, and selection bias.

How can I measure the quality of data labeling?

F1 Score: F1 score is the mean of precision and recall scores. Precision is the measure of how many of the algorithm’s outcomes are correct, while recall is the measure of how many outcomes were correct relative to how many were wrong. A score of one means 100% accuracy of precision, and hence a recall score of 1.

Inter-annotator Agreement/ Inter-rater Reliability: This is the measure of parity between labels. The inter-annotator agreement is especially important for models that make outcomes based on subjective evaluation, such as comment moderation. Statistical methods are used to measure inter-rater reliability.

What are some best practices for labeling data?

  • Create a Gold Standard: A gold standard is to data labeling what samples are to writing: they allow your data labeling team to shoot for an ideal. With a gold standard, you have a rubric to measure your team’s success every step of the way.
  • Use a Small Set of Labels: The more labels your data labeling team has to use, the more indecisive or confused they may get. This is a textbook example of the paradox of choice: the idea that the effort spent deliberating between two very similar choices outweighs the benefit of making the “right” choice.
  • Perform Ongoing Statistic Analysis: Ongoing analysis will help you identify outliers. While often the result of errors caused by the humans in the loop, genuine outliers, if found, are an indispensable source of information for your algorithm.
  • Use Multipass: What’s good for the goose is good for the gander! In the same way that running more simulations can lead to more accurate results, increasing the number of humans in the loop can lead to more accurately measured data.
  • Review Each Annotator: Give each annotator the same piece of data twice to evaluate their consistency. Do this multiple times with different data for greater accuracy.
  • Hire an Inclusive Team: By having an inclusive data labeling team, you increase the likelihood of reflecting your actual user base, which will be inclusive since it exists in the real world.
  • Iterate Continuously: Continuously update your data labeling to account for outliers and edge cases. This will make the outcomes of your model more accurate. Distribute the information throughout your team to use the multipass strategy and update your gold standard.

Is a managed team better than a crowdsourced team for data labeling creation?

Yes. Crowdsourcing makes management hard, especially when it comes to giving feedback. A managed team gives you more control, so you can respond to issues as they arise. You also build an internal culture, which makes work easy to do over time. The data proves this out: data science tech developer Hivemind determined that managed teams are more effective, faster, and only slightly more expensive.

What are some questions to ask a potential data labeling partner? 

  • Do you have dedicated success and project managers?
  • How will we communicate with your data labeling team?
  • How did you build your team?
  • Will we be working with the same data labelers the whole time and potentially throughout multiple projects?
  • If new team members are added, how are they trained?
  • Is your data labeling process flexible? How will you manage new enrichment points that we provide your data labelers?
  • How do you provide quality assurance?
  • How will you provide our team with quality metrics?
  • What happens if your data labelers fall short of expectations?
  • How involved in quality control will our team need to be?

Wrapping Up

Now that you know the essentials of training data, you’re on your way to a model that generates accurate outcomes. These strategies have withstood the test of time for a reason: they work! These tools have informed how A.I. Reverie generates its industry-specific models.

Just browse our results to see what A.I. Reverie can do for you!