Compared with the thrill of exploring the endless possibilities a machine learning model can bring to your business, the programming side of AI can seem a little tedious. As a result, it can be tempting to leave the finer points of training to your data scientists. However, training data is absolutely essential to the development of any machine learning model. The data you use will define your project, so a clear understanding of how it works will drastically improve your chances of success. We’ve briefly discussed AI training data on our website, but it’s worth explaining in a little more detail. Let’s dive into the world of training data and figure out why it’s so important.
What is training data?
Essentially, training data is the textbook that will teach your AI to do its assigned task, and will be used over and over again to fine-tune its predictions and improve its success rate. Your AI will use training data in several different ways, all with the aim of improving the accuracy of its predictions. It does this through the variables contained in the data. By identifying these and evaluating their impact on the algorithm, data scientists can strengthen the AI through copious adjustments. The best data will be extremely rich in detail, capable of improving your AI after hundreds of training cycles by hinting at a wide range of variables that affect your algorithm.
The majority of training data will contain pairs of input information and corresponding labeled answers, which are sometimes called the target. In some fields, it will also have highly relevant tags, which will help your AI to make more accurate predictions. However, since variables and relevant details are so important in the training process, datasets for different machine learning tasks will often look very different from each other. For example:
- In sentiment analysis, training data is usually composed of sentences, reviews or tweets as the input, with the label indicating whether that piece of text is positive or negative.
|The coffee here is great!||Positive sentiment|
|Not a fan of the cake, though.||Negative sentiment|
- In image recognition, the input would be the image, while the label suggests what is contained within the image.
- In spam detection, the input is an email or text message, while the label would provide information about whether the message is spam or not spam.
|Hi all – just writing to confirm that the meeting will be held at 12.00.||Not spam|
|Tokyo grandmas are making 2 million yen a month from this CRAZY scheme!!||Spam|
- In text categorization, sentences provide the input while the target would suggest the topic of the sentence, such as finance or law.
|Despite an early red card, the champions were two goals up at half-time.||Sports|
|If a new agreement is concluded between lessor and lessee, the terms of this contract shall be considered null and void.||Legal|
These are, of course, quite basic examples. It’s also possible to have multiple labels for one piece of raw input data, particularly if this data takes the form of long pieces of text like paragraphs, online comments, or even articles. In text categorization, for example, it’s possible to label each piece of data with multiple categories, depending on the classification system the annotator is using. For entity extraction, one paragraph could be annotated with several labels on the phrase or word level, in order to provide more information about the semantic meaning of phrases and the relationships between different sections of the text.
Looking at this, it quickly becomes clear that relevance and detail are a crucial element of good training data. If two different AI programs use the same training data, it will result in at least one crippled model. This is true even if both programs deal with the same broad category of input information, such as sentences. With this in mind, you can start thinking about the specific types of data and tags that you’ll need in order to provide your model with the best possible training.
The 3 types of data for machine learning
Despite the fact that most training data is straightforward in its composition, it’s not used as one homogenous mass. In fact, training is complex and involves several interlocking processes, all of which our dataset has to serve. There are three types of training data necessary to build a machine learning model, with each performing a different role.
Before going any further, it’s worth noting that the term ‘training data’ has two separate meanings. Just to complicate things, training data is not only used as an umbrella term for the total data needed for your project, but also refers to one of these specific subsets of data. This initially sounds confusing. However, the three types of data differ in several important ways. Once you’re aware of this, it should be easy to understand exactly what your data scientists are referring to.
- Training data is the part of your data which you use to help your machine learning model make predictions. Your model will be run on this set of data exhaustively, churning out results which your data scientists can use to develop your algorithm. It’s the largest part of your overall dataset, comprising around 70-80% of your total data used in the project.
- Validation data is a second set of data, also containing input and target information, which the machine learning model has never seen before. By running the model on validation data, it’s possible to see whether it can correctly identify relevant new examples. This is where it’s possible to discover new values that are impacting the process. Another common problem often identified during validation is overfitting, where the AI has been wrongly trained to identify examples that are too specific to the training data. As you can imagine, after validation, data scientists will often go back to the training data and run through it again, tweaking values and hyperparameters to make the model more accurate.
- Testing data comes into play after a lot of improvement and validation. While validation data has tags and target information left on as training wheels, testing data provides no help to the model at all. Asking the model to make predictions based on this data is meant to test whether it will work in the real world, where it won’t have helpful tags scattered around. The final test is the moment of truth for the model, to see if all the hard work has paid off.
It’s important to note that these three types of data work best together if they are all smaller parts of one, overarching dataset. This helps to ensure that all examples are consistent and relevant to the goals of the project. The complete pool of data should be both split and ordered into these 3 categories randomly to avoid selection bias.
Why is training data important?
Quite simply, without training data there is no AI. The cleanliness, relevance and quality of your data has a direct impact on whether your AI will achieve its goals. It’s best to think of training data in parallel with a human example of learning. Give a student an outdated textbook with half the pages missing and they won’t come close to passing their course. Similarly, without quality data, your AI will learn to do its job haphazardly, if at all. In the same way that you want to throw the weight of world-renowned professors behind your star pupil, your AI deserves the best data you can find, bursting with detailed tags and relevant annotations. Only then will your AI project catapult your business into the next stage of development.
When you’re ready for the custom datasets you’ll need, be sure to check out Lionbridge AI. Our crowdsourcing platform is primed to improve the quality of your data. For more information, read about our data annotation services to discover how we can help you train your model to its full potential.