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Inductive reasoning is the use of evidence to propose a theory, or in other words, assuming a given outcome from past outcomes or other available data. Inductive reasoning is probabilistic or uncertain in the sense that it relies on the given data instead of other types of discovery.
In its various forms, inductive reasoning is the fundamental engine of machine learning systems. The typical machine learning system takes in data in the form of training sets, and uses that available data to produce probabilistic conclusions.
Inductive reasoning is often contrasted to deductive reasoning, which uses strong logical conditions to draw conclusions. Unlike deductive reasoning, inductive reasoning uses the evidence to postulate or theorize a conclusion, so it is not a "guaranteed reasoner." Apart from its widespread use in machine learning, inductive reasoning also plays a role in examining how neural networks mimic human cognitive ability and how the neurons or units of processing use probabilistic inputs to determine outcomes.
Types of inductive reasoning include simple induction, generalization, statistical syllogism and arguments related to contrasting analogies.