Add to ORKGIn this talk, I will review various ways of evaluating models learned from data, starting from simple measures, such as accuracy, sensitivity and specificity, through more complex measures, such as ROC curves and calibration curves used in probabilistic systems, and finally confidence intervals over the results obtained from models and over evaluation measures
Traditional measures for assessing the performance of classification models for binary outcomes are ...
Computational models of learning and the theories they represent are often validated by calibrating ...
<p>The evaluation measures are precision, recall, F1 measure, and RI values, and the different metho...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
<p>Six models (SVM, RRF, PLR, NNET, KNN, and CART) were tested for their performance using three par...
Results of sensitivity analyses across different splits of the training and test sets. We created 1,...
The measurement and reporting of model error is of basic importance when constructing models. Here, ...
AbstractFrequently, scientific findings are aggregated using mathematical models. Because models are...
In model development, model calibration and validation play complementary roles toward learning reli...
By validation I mean comparing values generated by a model with actual values to determine how well ...
Given a sequence of measurements, a statistical model is a proposed solution to the inverse problem....
Model diagnostics and forecast evaluation are two sides of the same coin. A common principle is that...
We discuss tools for the evaluation of probabilistic forecasts and the critique of statistical model...
There has been a growing recognition that issues of data quality, which are routine in practice, can...
This paper is about constructing confidence bands around ROC curves. We first introduce to the machi...
Traditional measures for assessing the performance of classification models for binary outcomes are ...
Computational models of learning and the theories they represent are often validated by calibrating ...
<p>The evaluation measures are precision, recall, F1 measure, and RI values, and the different metho...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
<p>Six models (SVM, RRF, PLR, NNET, KNN, and CART) were tested for their performance using three par...
Results of sensitivity analyses across different splits of the training and test sets. We created 1,...
The measurement and reporting of model error is of basic importance when constructing models. Here, ...
AbstractFrequently, scientific findings are aggregated using mathematical models. Because models are...
In model development, model calibration and validation play complementary roles toward learning reli...
By validation I mean comparing values generated by a model with actual values to determine how well ...
Given a sequence of measurements, a statistical model is a proposed solution to the inverse problem....
Model diagnostics and forecast evaluation are two sides of the same coin. A common principle is that...
We discuss tools for the evaluation of probabilistic forecasts and the critique of statistical model...
There has been a growing recognition that issues of data quality, which are routine in practice, can...
This paper is about constructing confidence bands around ROC curves. We first introduce to the machi...
Traditional measures for assessing the performance of classification models for binary outcomes are ...
Computational models of learning and the theories they represent are often validated by calibrating ...
<p>The evaluation measures are precision, recall, F1 measure, and RI values, and the different metho...