A computer is trained using a given data set in machine learning, and it uses this training to predict the properties of a given new data set. For instance, we could teach a computer by showing it 1000 photographs of cats and 1000 more images that aren't of cats, asking it each time if the image is of a cat or not. The computer should be able to determine whether or not this new image is a cat if we show it to it using the training described above.
Specialized algorithms are used during the training and prediction processes. An algorithm receives the training data, which it then utilizes to make predictions about fresh test data. K-Nearest-Neighbor classification is one of them (KNN classification). It starts with a test data and extracts the k closest data values from the test data set. Then it chooses the neighbor with the highest frequency and provides its properties as the outcome of the forecast.
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 BULB, 'Write to Earn. Read to Earn' (online, 2022) <https://www.bulbapp.io/>