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Inductive bias

The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g., step-functions in decision trees instead of continuous functions…

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Types

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Inductive bias

Nodes19
Edges18
Triples10
Avg. degree1.89
Density0.105263
Components1

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Inductive bias

Top relations

related to Types · 9
Inductive bias → Although, Bayesian, Given, Maximum, Minimum, Naive Bayes, Nearest, The, This
is a · 1
Inductive bias → logical formula that

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Important terminology

bias inductive learning algorithm hypothesis data learner output given cases assumptions target training examples algorithms predict outputs learn one another

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Inductive biasis alogical formula that0.90text
Inductive biasrelated to TypesThe0.60section
Inductive biasrelated to TypesMaximum0.60section
Inductive biasrelated to TypesBayesian0.60section
Inductive biasrelated to TypesThis0.60section
Inductive biasrelated to TypesNaive Bayes0.60section
Inductive biasrelated to TypesMinimum0.60section
Inductive biasrelated to TypesAlthough0.60section
Inductive biasrelated to TypesNearest0.60section
Inductive biasrelated to TypesGiven0.60section

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