Technically speaking, the terms supervised and unsupervised learning refer to whether the raw data used to create algorithms has been prelabeled or not. In supervised learning, data scientists feed algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified in the training data. For example, if you are trying to train an algorithm to infer if a picture has a cat in it using supervised learning, data scientists create a label for each picture used in the training data indicating whether the image contains a cat or not. In an unsupervised learning approach, the algorithm is trained on unlabeled data.
Jul-24-2020, 05:10:17 GMT