![]() ![]() Where the labeling model has lower confidence in its results, it will pass the data to humans to do the labeling. Where the labeling model has high confidence in its results based on what it has learned so far, it will automatically apply labels to the raw data. In this process, a machine learning model for labeling data is first trained on a subset of your raw data that has been labeled by humans. To overcome this challenge, labeling can be made more efficient by using a machine learning model to label data automatically. The majority of models created today require a human to manually label data in a way that allows the model to learn how to make correct decisions. But, the process to create the training data necessary to build these models is often expensive, complicated, and time-consuming. Successful machine learning models are built on the shoulders of large volumes of high-quality training data. In machine learning, a properly labeled dataset that you use as the objective standard to train and assess a given model is often called “ground truth.” The accuracy of your trained model will depend on the accuracy of your ground truth, so spending the time and resources to ensure highly accurate data labeling is essential. The machine learning model uses human-provided labels to learn the underlying patterns in a process called "model training." The result is a trained model that can be used to make predictions on new data. The tagging can be as rough as a simple yes/no or as granular as identifying the specific pixels in the image associated with the bird. For example, labelers may be asked to tag all the images in a dataset where “does the photo contain a bird” is true. ![]() Data labeling typically starts by asking humans to make judgments about a given piece of unlabeled data. For supervised learning to work, you need a labeled set of data that the model can learn from to make correct decisions. ![]() Today, most practical machine learning models utilize supervised learning, which applies an algorithm to map one input to one output. ![]()
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