For regression problems, the R2, mean absolute error and root mean squared error should be reported68, while for classification problems, the accuracy, precision, recall, balanced accuracy or F1 score, and kappa
In regression analyses, fixation duration within AOI was positively associated with accuracy [beta-coefficients 28.9 standardized error (SE) 6.42, P = 0.002). Total time spent viewing the videos was also significantly associated with accuracy (beta-coefficient 5.08, SE 0.59, P < 0.0001). For ...
Still, the remaining characteristics do not have an adequate correlation, therefore it was not feasible to establish a linear regression model for predicting water quality. Nevertheless, the WQI may exhibit a non-linear correlation with the water quality parameters. In this study, the authors choose...
This chapter introduces the use of regression to interpret imagery. Regression is one of the fundamental tools you can use to move from viewing imagery to analyzing it. In the present context, regression means predicting a numeric variable for a pixel instead of a categorical variable, such as ...
An explanation is the collection of features of the interpretable domain, that have contributed for a given example to produce a decision (e.g. classification or regression). The features that form the explanation can be supplemented by relevance scores indicating to what extent each feature contrib...
Bill Gould wrote a blog post in 2011 titled "Use poisson rather than regress; tell a friend". He recommends that we abandon the practice of linear regression with log-transformed dependent variables and instead use Poisson regression with robust standard errors. I won't reiterate his reasoning ...
(binary classification) andSuperconductor(regression). All the evaluations were run in parallel on all available cores in Azure Virtual Machine with size Standard_D8_v3 (8 cores and 32GB memory) (except forscikit-learnmodels inSHAPpackage). We ran each evaluation on 10,000 samples, and the ...
2004. Computing Interaction Effects and Standard Errors in Logit and Probit Models. The Stata Journal 4(2): 154-167. Rainey, Carlisle. 2016. “Compression and Conditional Effects: A Product Term Is Essential When Using Logistic Regression to Test for Interaction.” Political Science Research and ...
that removing the within-group means and estimating a regression on the deviations without an intercept (as given in equation 3) produces the same coefficients but different standard errors. How can method 3 be wrong? Because it fails to account for the fact that the means we removed are *ES...
A simple new approach to variable selection in regression, with application to genetic fine mapping. J R Stat Soc Ser B. 2020;82(5):1273–300. Google Scholar Bauer S, Gagneur J, Robinson PN. GOing Bayesian: model-based gene set Analysis of Genome-Scale Data. Nucleic Acids Res. 2020;...