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  2. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    In the simulation of energy consumption of buildings, the RMSE and CV(RMSE) are used to calibrate models to measured building performance. [9] In X-ray crystallography, RMSD (and RMSZ) is used to measure the deviation of the molecular internal coordinates deviate from the restraints library values.

  3. Mean absolute error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_error

    In particular, m is a sample median if and only if m minimizes the arithmetic mean of the absolute deviations. [ 7 ] More generally, a median is defined as a minimum of E ( | X − c | − | X | ) , {\displaystyle E(|X-c|-|X|),} as discussed at Multivariate median (and specifically at Spatial median ).

  4. Mean squared prediction error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_prediction_error

    First, with a data sample of length n, the data analyst may run the regression over only q of the data points (with q < n), holding back the other n – q data points with the specific purpose of using them to compute the estimated model’s MSPE out of sample (i.e., not using data that were used in the model estimation process).

  5. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    The MSE could be a function of unknown parameters, in which case any estimator of the MSE based on estimates of these parameters would be a function of the data (and thus a random variable). If the estimator θ ^ {\displaystyle {\hat {\theta }}} is derived as a sample statistic and is used to estimate some population parameter, then the ...

  6. Mean absolute scaled error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_scaled_error

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  7. 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. [1]

  8. Minimum mean square error - Wikipedia

    en.wikipedia.org/wiki/Minimum_mean_square_error

    Also, the gain factor, +, depends on our confidence in the new data sample, as measured by the noise variance, versus that in the previous data. The initial values of x ^ {\displaystyle {\hat {x}}} and C e {\displaystyle C_{e}} are taken to be the mean and covariance of the aprior probability density function of x {\displaystyle x} .

  9. Root mean square - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square

    The RMS over all time of a periodic function is equal to the RMS of one period of the function. The RMS value of a continuous function or signal can be approximated by taking the RMS of a sample consisting of equally spaced observations. Additionally, the RMS value of various waveforms can also be determined without calculus, as shown by ...