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  2. Loss functions for classification - Wikipedia

    en.wikipedia.org/wiki/Loss_functions_for...

    Given the binary nature of classification, a natural selection for a loss function (assuming equal cost for false positives and false negatives) would be the 0-1 loss function (0–1 indicator function), which takes the value of 0 if the predicted classification equals that of the true class or a 1 if the predicted classification does not match ...

  3. Loss function - Wikipedia

    en.wikipedia.org/wiki/Loss_function

    Leonard J. Savage argued that using non-Bayesian methods such as minimax, the loss function should be based on the idea of regret, i.e., the loss associated with a decision should be the difference between the consequences of the best decision that could have been made under circumstances will be known and the decision that was in fact taken before they were known.

  4. Win–stay, lose–switch - Wikipedia

    en.wikipedia.org/wiki/Win–stay,_lose–switch

    In psychology, game theory, statistics, and machine learning, win–stay, lose–switch (also win–stay, lose–shift) is a heuristic learning strategy used to model learning in decision situations. It was first invented as an improvement over randomization in bandit problems . [ 1 ]

  5. Coupling (computer programming) - Wikipedia

    en.wikipedia.org/wiki/Coupling_(computer...

    A module here refers to a subroutine of any kind, i.e. a set of one or more statements having a name and preferably its own set of variable names. Content coupling (high) Content coupling is said to occur when one module uses the code of another module, for instance a branch. This violates information hiding – a basic software design concept.

  6. Hinge loss - Wikipedia

    en.wikipedia.org/wiki/Hinge_loss

    The plot shows that the Hinge loss penalizes predictions y < 1, corresponding to the notion of a margin in a support vector machine. In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). [1]

  7. Catastrophic cancellation - Wikipedia

    en.wikipedia.org/wiki/Catastrophic_cancellation

    In numerical analysis, catastrophic cancellation [1] [2] is the phenomenon that subtracting good approximations to two nearby numbers may yield a very bad approximation to the difference of the original numbers.

  8. Loose coupling - Wikipedia

    en.wikipedia.org/wiki/Loose_coupling

    Loose coupling occurs when the dependent class contains a pointer only to an interface, which can then be implemented by one or many concrete classes. This is known as dependency inversion . The dependent class's dependency is to a "contract" specified by the interface; a defined list of methods and/or properties that implementing classes must ...

  9. Hamming distance - Wikipedia

    en.wikipedia.org/wiki/Hamming_distance

    For a fixed length n, the Hamming distance is a metric on the set of the words of length n (also known as a Hamming space), as it fulfills the conditions of non-negativity, symmetry, the Hamming distance of two words is 0 if and only if the two words are identical, and it satisfies the triangle inequality as well: [2] Indeed, if we fix three words a, b and c, then whenever there is a ...