For instance, the observations are always assumed to be at the tail end of the causal chains (Petrie, 1996). In actual practice, the paradigms for supervised learning often leave the important probabilities for numerous inputs undefined. If input variables are available, the model works well, but if not, it is difficult to make sense or to infer anything about the outputs (Stork, 2001).
In the lay world, we can think of supervised learning as a computer program that allows an individual to learn a language, say Spanish. The pronunciation of the letters is the output. Each time the right letter is produced; feedback either strengthens or weakens it for a wrong letter. This has about an eighty percent chance of producing correct words. For individuals, feedback is akin to taking a walk and getting poison ivy; exposure to the plants teaches one where to walk, to wear appropriate clothing, and to bring antihistamine cream. In real life situations, too, supervision aids quicker results whereas in unsupervised learning trial and error are the main mode of learning (Goudbeek, et.al., 2006).
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