Data labeling provides users with greater context, quality, and efficiency across industries.
Higher accuracy in predictions: within machine learning algorithms, properly labeled data allows the model to train and yield the expected output. Moreover, it provides the βground truthβ for testing and repeating subsequent models.
Better data usage: Data labeling can also improve usability of data variables within a model. For instance, re-categorizing categorical variables into binary ones in order to make it more consumbale for a model. Sorting data in this way helps optimize the model by reducing the number of model variables or enabling the inclusion of control variables.
