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Elements Of The HITL Machine learning models can't be designed to be flawless - they're improved over time, with the training process and tests. A ML algorithm, in order to be able to make precise predictions, must be trained using massive amounts of extremely accurate training data. Over time, and following several trials and error it should be capable of come up with the desired output. Achieving greater accuracy in predictions is dependent in the high-quality of the training data that you feed to the software. The training data you feed into the system is the highest quality only if it is correct, organized and annotated and is pertinent to the task. It is crucial to engage humans in the annotation, labeling and fine tune the model. Human-in-the-loop method permits human involvement in labeling, classifying and labeling the data, and evaluating the model. Particularly in situations where the algorithm isn't confident in making a correct prediction, or is overconfident re...