Publication date
June 2018
DOIAbstract
An introduction to model assessment focusing on cross-validation, goodness of fit statistics, bias, and precision
An introduction to model assessment focusing on cross-validation, goodness of fit statistics, bias, and precision
<p>The tradeoff between overfit and underfit for one of the five cross-validation data splits. Model...
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
To validate an estimated model and to have a good understanding of its reliability is a central aspe...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
Before any model can be used with confidence it must be tested to assess its fitness for the given t...
An introduction to model fitting, starting with a review of the linear model and proceeding to a def...
Models are created by people and people make mistakes. For this reason it is necessary to validate b...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
In this introduction, we set the context for this special issue on modern goodness of fit methods, o...
AbstractFrequently, scientific findings are aggregated using mathematical models. Because models are...
Accuracy measures for 10-fold cross-validation of Model 1 using the entire feature set for predictio...
Cross-validation is the process of comparing a model’s predictions to data that were not used in the...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
Goodness-of-fit indices and model comparisons for measurement invariance models.</p
<p>Accuracy of the models in the test phase and the 10-fold cross-validation.</p
<p>The tradeoff between overfit and underfit for one of the five cross-validation data splits. Model...
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
To validate an estimated model and to have a good understanding of its reliability is a central aspe...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
Before any model can be used with confidence it must be tested to assess its fitness for the given t...
An introduction to model fitting, starting with a review of the linear model and proceeding to a def...
Models are created by people and people make mistakes. For this reason it is necessary to validate b...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
In this introduction, we set the context for this special issue on modern goodness of fit methods, o...
AbstractFrequently, scientific findings are aggregated using mathematical models. Because models are...
Accuracy measures for 10-fold cross-validation of Model 1 using the entire feature set for predictio...
Cross-validation is the process of comparing a model’s predictions to data that were not used in the...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
Goodness-of-fit indices and model comparisons for measurement invariance models.</p
<p>Accuracy of the models in the test phase and the 10-fold cross-validation.</p
<p>The tradeoff between overfit and underfit for one of the five cross-validation data splits. Model...
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
To validate an estimated model and to have a good understanding of its reliability is a central aspe...
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