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Measures for Regression Tasks Name Explanation MSE and RMSE The mean square error (MSE) and root mean squared error (RMSE) of a regression model are popular ways to quantify the difference between the predictor and the true value.
The difference between the predictor variable and the permuted predictor variable prediction error is averaged over all trees and normalized by the standard deviation of differences.
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Furthermore, to investigate if the regression coefficients of the predictor variables differed between models, we used the approach by Cohen32 in which the difference between the predictors' regression weights is divided by the standard error of the difference and the resulting z-score is tested for significance.
(3) Is there any difference between the predictors for reading comprehension in Chinese dyslexic and typically developing children in junior and senior elementary grades?
The difference between the predictors of recovery found for CAT and CCQ can be explained in part by the different types of analysis; however, there are also some differences between them.
There were limited differences between the predictors selected in the FIRENPP and FIREnoNPP ensembles (Table 2) and between the spatial distributions of expected fire probabilities (Figures 1C and 1D).
Existing literature converges on extensive differences between the predictors of subjective and objective QoL.
There were few differences between the predictors for these two composite prescribing indicators (P1-P13 v P1-P13 plus P16-P19) except, as expected, on sex and age (as a result of combined hormone contraceptive prescribing occurring mostly in the younger age group).
The main differences between the predictors usually rely in the set of D and ND structures adopted to train the method (for example, only those structures with ligands that are drugs known to be orally available) and the specific subset of all possible pocket descriptors that were considered.
Activity across the entire striatal ROI was significantly associated with unique MS-PE (t(18)=4.172, P<0.001, Cohen's d=0.957) but not with unique ERO-PE (t(18)=1.162, P=0.260, Cohen's d=0.266), although the difference between the two predictors did not reach significance (t(18)=1.558, P=0.137, Cohen's d=0.357).
As far as theory is concerned, we derive a heuristic rule of thumb which relates non-uniqueness of the predictor weights to the difference between the number of predictors and the number of donor units that synthesize the unit of interest in the training period.
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