One of the first steps in creating a successful machine learning model is to gather high-quality data. State-of-the-art models like neural networks need a lot of training data in order to perform well. The truth is, before proceeding with any model training or any classification problem definition, you need a sufficiently large set of correctly labeled data to describe your problem. Oftentimes, this data needs to be labeled manually. Just imagine how much money or time (or both) it would actually take to do this! Let’s have a look at how “Active Learning“ helps solve this problem.
What is Active Learning
Let’s say that you want to create a classifier that can distinguish whether an image shows a cat or a dog. To do this, somebody has to go through hundreds, maybe thousands of images and specify for each of them whether it is a cat or a dog. Consequently, adding labels to unlabeled datasets is a very time-intensive process that requires extensive human labor.
That is when Active Learning comes into the spotlight! Thanks to its intelligent capability, it is capable of ordering and prioritizing the data which needs to be labeled. For example, more informative samples will be labeled first. Active Learning can be used in situations where the amount of data is too large to be labeled and some priority needs to be made to label the data in a smart way. As result, the total number of data points required for analysis can often be much lower than in normal supervised learning. The advantage of Active Learning is that it can be used for basically any use case where we need labeled data, such as object detection, text classification and sentiment analysis.
How Active Learning works
One way to do Active Learning is to look at the uncertainty of the model. This approach lets the human work together with the model by first letting them label a small subset of the unlabeled data. After this data is labeled, it is used to train the model as usual. Then, we let the model predict the labels for the rest of the unlabeled data.
Assume that there are a few images where the model is 90% certain about which animal it depicts. However, there are also some images where the model is only 55% certain. In this case, it makes sense to let the human help the model label those images in which the model is uncertain. Therefore, we order the samples based on how uncertain the model is about those samples. The labeler can now continue labeling the next subset of images and this loop continues until we are happy with the model’s performance.
Another way to sort the samples is by taking the previous approach but feeding the unlabeled data into multiple models instead. Then, we can look at the disagreement between these models, instead of the uncertainty of only one. For example, there is one image where all models agree that it is a cat, and there is another image where half of the models thinks it is a cat and the other half thinks it is a dog. Then, the most efficient thing to do is let the human label the sample that the models disagree on. Again, the samples are sorted and the labeling loop continues.
Benefits of using Active Learning
Labeling data accurately can be tedious, time-consuming and prone to human error when it comes to working with large quantities of data. Active Learning helps a lot when there is a shortage of labeled data and provides you with the following benefits:
It reduces the amount of labeled data that is needed and the experts required to accurately label them. Helping you spend less time and money.
It provides you with faster feedback on your model performance.
It helps you produce higher-performing models.
Do you want to know more about what Active Learning and other Artificial Intelligence fields can offer to your business? Our team of Data Science experts is always ready to help you!
