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What is data labeling for machine learning?

Kayley Marshall
Kayley MarshallAnswered

Data labeling in machine learning is the process of detecting raw data and adding one or more relevant labels to provide context an ML model may learn from. For several use cases such as NLP, computer vision, and speech recognition, data labeling is necessary.

Supervised learning, in which an algorithm is used to map between input and output, is currently the most used method for practical machine learning models. A labeled collection of data that the model can learn from and use to make wise judgments is necessary for supervised learning to function. A common starting point for machine learning labeling is to solicit opinions from people regarding a certain set of unlabeled data. Labelers might be required, for instance, to tag all the pictures in a collection with the value true for “does the picture contain a bird.” The tagging might be as basic as yes or no or as detailed as identifying the precise pixels in the bird’s image. In model training, the ML model employs labels provided by humans to discover the underlying patterns. The outcome is a learned model that can be applied to new data to create predictions.

In machine learning, Ground Truth is an appropriately labeled dataset that is utilized as the objective benchmark for training and evaluating a particular model. To produce a reliable, trained model, it is crucial to invest time and energy into obtaining correct ground truth labels.

The effectiveness and accuracy of the data labeling process can be increased using a variety of ways:

  • Interfaces that are both simple and effective can reduce the mental strain placed on human labelers and the number of times they need to move between tasks.
  • Labeler consensus to assist balance the bias/error of different annotators.
  • Review labels to make sure they are accurate and to make any required updates.
  • Active learning identifies the most beneficial data that has to be categorized by humans using machine learning.
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