How to collect, label and annotate it and why it matters just as much as your machine learning algorithm
Algorithms learn from data. They find relationships, develop understanding, make decisions, and evaluate their confidence from the training data they’re given. And the better the training data is, the better the model performs.
In fact, the quality and quantity of your training data has as much to do with the success of your data project as the algorithms themselves.
Now, even if you’ve stored a vast amount of well-structured data, it might not be labeled in a way that actually works for training your model. For example, autonomous vehicles don’t just need pictures of the road, they need labeled images where each car, pedestrian, street sign and more are annotated; sentiment analysis projects require labels that help an algorithm understand when someone’s using slang or sarcasm; chatbots need entity extraction and careful syntactic analysis, not just raw language.
In other words, the data you want to use for training usually needs to be enriched or labeled. Or you might just need to collect more of it to power your algorithms. But chances are, the data you’ve stored isn’t quite ready to be used to train your classifiers.
Because if you’re trying to make a great model, you need great training data. And we know a thing or two about that. After all, we’ve labeled over 5 billion rows of data for some of the most innovative companies in the world. Whether it’s images, text, audio, or, really, any other kind of data, we can help create the training set that makes your models successful.
What kind of training data can you create on Figure Eight?
Here are a few of the use cases we do best:
Datasets curated on the Figure Eight platform. Free to download for the entire data science and machine learning community.
Simply put, training data is used to train an algorithm. Generally, training data is a certain percentage of an overall dataset along with testing set. As a rule, the better the training data, the better the algorithm or classifier performs.
Once a model is trained on a training set, it’s usually evaluated on a test set. Oftentimes, these sets are taken from the same overall dataset, though the training set should be labeled or enriched to increase an algorithm’s confidence and accuracy.
Generally, training data is split up more or less randomly, while making sure to capture important classes you know up front. For example, if you’re trying to create a model that can read receipt images from a variety of stores, you’ll want to avoid training your algorithm on images from a single franchise. This will make your model more robust and help prevent it from overfitting.
There’s really no hard-and-fast rule around how much data you need. Different use cases, after all, will require different amounts of data. Ones where you need your model to be incredibly confident (like self-driving cars) will require vast amounts of data, whereas a fairly narrow sentiment model that’s based off text necessitates far less data. As a general rule of thumb though, you’ll need more data than you’re assuming you will.
Our Data for Everyone page is updated routinely and contains a wide variety of training data, from labeled images and text to audio files and video. We’ve also compiled a list of our 10 Favorite Open Data Libraries as an additional resource.
Big data and training data are not the same thing. Gartner calls big data “high-volume, high-velocity, and/or high-variety” and this information generally needs to be processed in some way for it to be truly useful.
Training data, as we mentioned above, is labeled data used to teach AI models or machine learning algorithms.