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  2. Error function - Wikipedia

    en.wikipedia.org/wiki/Error_function

    This expression is valid only for positive values of x, but it can be used in conjunction with erfc x = 2 − erfc(−x) to obtain erfc(x) for negative values. This form is advantageous in that the range of integration is fixed and finite.

  3. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    Definition and basic properties. The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).

  4. Circular error probable - Wikipedia

    en.wikipedia.org/wiki/Circular_error_probable

    Thus the MSE results from pooling all these sources of error, geometrically corresponding to radius of a circle within which 50% of rounds will land. Several methods have been introduced to estimate CEP from shot data.

  5. Loss function - Wikipedia

    en.wikipedia.org/wiki/Loss_function

    In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture.

  6. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

    The use of the MAPE as a loss function for regression analysis is feasible both on a practical point of view and on a theoretical one, since the existence of an optimal model and the consistency of the empirical risk minimization can be proved.

  7. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    One can then also calculate the mean square of the model by dividing the sum of squares of the model minus the degrees of freedom, which is just the number of parameters. Then the F value can be calculated by dividing the mean square of the model by the mean square of the error, and we can then determine significance (which is why you want the ...

  8. Error analysis (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Error_analysis_(mathematics)

    Stability is a measure of the sensitivity to rounding errors of a given numerical procedure; by contrast, the condition number of a function for a given problem indicates the inherent sensitivity of the function to small perturbations in its input and is independent of the implementation used to solve the problem.

  9. Mean absolute error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_error

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