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Discover LudwigThe phrase "average squared prediction" is correct and usable in written English.
It can be used in contexts related to statistics, data analysis, or machine learning, particularly when discussing the performance of predictive models.
Example: "To evaluate the model's accuracy, we calculated the average squared prediction of the test dataset."
Alternatives: "mean squared prediction" or "mean squared estimate".
Exact(2)
For each model we also report the adjusted rsquared and the root mean square error (RMSE) (the RMSE is the positive square root of the average squared prediction error).
The root mean square error (RMSE; the positive square root of the average squared prediction error, which attaches greater weight to larger errors) and adjusted R were also reported.
Similar(58)
rmspe denotes the root mean squared prediction error.
The design criterion is the average mean squared prediction error (AMSPE).
Finally, we performed 10-fold cross validation to determine the level of α that minimized the average mean squared prediction error in our cross validation test sets to select the sub-tree that was least likely to overfit the data and most likely to perform similarly in a new dataset.
Mean Square Error of Prediction is the average of the squared prediction errors of MEBV.
MSE: mean squared prediction error.
RMSPE: the squared root of the mean of the squared prediction errors.
The model predicted drinking water intake, fecal, urinary, and total fresh manure water output with root mean square prediction errors as a percentage of average values of 18.1, 15.6, 30.6, and 14.6%, respectively.
For the numerical assessment a classical mean square prediction error (weighted root mean square over years of the prediction error in the average quantity shown, weighted by the number of patients contributing data in the given year) and the likelihood, resulting from the maximum likelihood algorithm were calculated for each of the covariate models to allow comparison.
Here, "better predicted" means a smaller mean square prediction error.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com