HITL describes the process by which an algorithm allows the best results to be generated each time a machine or computer system is unable to solve a problem, such as the need for human intervention, such as the training and testing steps involved in building an algorithm. .
If you have a sufficient number of datasets, after learning from these datasets, an ML algorithm can make decisions easily and accurately. But before that, you need to learn from a certain quantity and quality of machine data sets, how to correctly identify the right criteria and thus get the right results.
This is where human-in-the-loop machine learning is used, creating a continuous circle with a combination of human and machine intelligence, where ML algorithms are trained, tested, tuned and validated. In this loop, with the help of humans, the machine becomes smarter, more trained and confident to make decisions quickly and accurately when used in real life, and also helps to train algorithms.
Applications
The Human-in-the-Loop combines two machine learning algorithm processes – supervised and supervised learning. In supervised machine learning, data sets labeled or interpreted by ML experts are used to train algorithms to make accurate predictions when used in real life.
On the other hand, the Learning Algorithm is not labeled on unattended machine learning. It is left to its own devices to find the structure at its input and to remember the data in its own way. In HITL, initially, humans label training data for algorithms, which are then fed into algorithms that can be understood by machines.
Later, humans test and evaluate the results and predictions for ML model evaluation, and if the results are inaccurate, humans tune in to algorithms or re-examine the data to make accurate predictions.
Influence on machine learning
It is not possible to perform a machine learning process without human inputs. Algorithms cannot be studied at all unless they are given according to their suitability. For example, a machine learning model cannot understand raw data if humans do not explain and understand it to machines.
Here, the data labeling process is the first step in creating a reliable model trained by the algorithm, especially when the data is available in a non-structured format. An algorithm cannot detect unstructured data, such as text, audio, video, images, and other content that is not properly labeled.
Human-in-the-loop is not an idea you can implement in every machine learning project. The HITL approach is mainly used when no more data is available. Human-in-loop is ideal because, at this point, people can make better judgments than machines are capable of in the beginning.
With this, humans build machine learning training data sets to help them learn the machine from such data.
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