Sentence examples for sum of predictions from inspiring English sources

The overall number of prevalent cases across all age groups can easily be estimated as a sum of predictions over individual age categories.

The sum of prediction scores is defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ S_{p}^{\text{N}} $$\end{document}.

If the sum of prediction scores over those individuals who have the corresponding genotype combination is greater than a threshold value, assign 'high-risk' to the cell corresponding to the genotype combination.

For a multivariate, multistrain decomposition involving design matrix D = (d1, … ,  d n T, we apply a property of the hat matrix, P = D[ DT D]−1 DT, which is also known as a "projection" matrix, to decompose the sum-of-squared prediction error into modeled components and noise (Neter et al. 1996).

In an alternating decision tree, unlike a decision tree, the classification result is the sign of the sum of the predictions along the path, instead of the label of the leaf.

Unlike original decision trees: an instance is mapped into a path along the tree from the root to one of the leaves and output is the label of the leaf, the classification result of an alternating decision tree became the sign of the sum of the predictions along the multi-path associated with the given instances.

Although requiring additional data and at the cost of additional training, a weighted sum of the predictions of the individual classifiers was considered.

Apparently, the weighted sum of domain predictions is higher than the whole-chain GDT-TS for this example.

The sum of these predictions should yield an overall relative concentration that is more accurate (provided that the ROI is at least partially resolved) than the estimate measured from any individual ROI.

Therefore, the annual average pollution at any location was predicted using the sum of the prediction from the long-term average surface/GIS-derived covariates and the prediction from the calendar-year specific residual spatial variability surface.

The ML method estimates the parameters in a prediction error settings, i.e. the sum of squared prediction error is minimized.