![]() ![]() That old saying if you want it done right, do it yourself expresses one of the key reasons to choose an internal approach to labeling. The pros and cons of different data labeling approaches In-house labeling The choice of an approach depends on the complexity of a problem and training data, the size of a data science team, and the financial and time resources a company can allocate to implement a project. ![]() Okay, how do you get labeled data? Data labeling approachesĭata labeling can be performed in a number of different ways. This is something known as the Human-in-the-Loop model when specialists (data annotators and data scientists) prepare the most fitting datasets for a certain project and then train and fine-tune the AI models. In many cases, data labeling tasks require human interaction to assist machines. This process is one of the stages in preparing data for supervised machine learning.įor example, if your model has to predict whether a customer review is positive or negative, the model will be trained on a dataset containing different reviews labeled as expressing positive or negative feelings.īy the way, you can learn more about how data is prepared for machine learning in our video explainer. What is data labeling?īefore diving into the topic, let’s discuss what data labeling is and how it works.ĭata labeling (or data annotation) is the process of adding target attributes to training data and labeling them so that a machine learning model can learn what predictions it is expected to make. Here we will talk more about this process, its approaches, techniques, and tools. We briefly described data labeling in the article about the general structure of a machine learning project. How to get a high-quality labeled dataset without getting grey hair? The main challenge is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use. Labelers must be extremely attentive because each mistake or inaccuracy negatively affects a dataset’s quality and the overall performance of a predictive model. An algorithm can only find target attributes if a human mapped them. Historical data with predefined target attributes (values) is used for this model training style. Labeling is an indispensable stage of data preprocessing in supervised learning. While labeling is not launching a rocket into space, it’s still serious business. For nine years, its contributors manually annotated more than 14 million images. ImageNet - an image database - would deserve its own style. The labelers’ monument could be Atlas holding that large rock symbolizing their arduous, detail-laden responsibilities. If there was a data science hall of fame, it would have a section dedicated to the process of data labeling in machine learning. Data labeling best practices Reading time: 16 minutes.
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