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Therefore, we focus on prediction error degree estimation based on ordinal regression.
In this paper, we try to perform accurate prediction error degree estimation based on ordinal regression.
From this point of view, we focus on error degree estimation based on ordinal regression using many training examples.
Section 2.1 presents details of feature extraction from meteorological data, and Section 2.2 presents an algorithm for estimating the prediction error degree based on ordinal regression using MKDA.
Analyses were based on ordinal regression methods, assuming an underlying continuous latent variable, using either the proportional odds (PO) or the proportional hazards (PH) models.
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In this study, a predictive model for soil erosion degree, based on ordinal logistic regression, has been developed and evaluated using data from three large forest fires in South-eastern Spain. South-eastern Spain
It is possible that imputation methods based on ordinal logistic regression, or similar methods for imputing ordinal data would perform well for ordinal variables with a small number of categories.
Based on ordinal logistic regressions, false positives in the highest 25%50%0% for the CEA Registry) had between 1.9 and 6.1 higher odds of being deemed "clearly relevant" rather than "maybe" or "maybe" rather than "clearly irrelevant" upon re-review by human experts.
A diagnosis approach based on ordinal ranking and the proposed feature selection method is then introduced.
The scale is treated as an interval scale based on ordinal level item scoring [ 3].
Possible relationships based on ordinal data were tested using Spearman's correlation coefficient.
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based on ordinal level
based on ordinal scale
based on nonlinear regression
based on ordinal deviation
based on trigonometric regression
based on logistic regression
based on ordinal utility
based on ordinal spacing
based on ordinal measure
based on Boosted regression
based on ordinal ranking
based on ordinal grading
based on local regression
based on multiple regression
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