Publication date
January 2023
DOIAbstract
Basic performance measures for classification models are presented along with general selection guidelines and a discussion of the costs associated to a bad prediction
Basic performance measures for classification models are presented along with general selection guidelines and a discussion of the costs associated to a bad prediction
The area under the ROC (Receiver Operating Characteristic) curve, or simply AUC, has been widely u...
Prediction models are becoming more and more important in medicine and cardiology. Nowadays, specifi...
Classifiers that are deployed in the field can be used and evaluated in ways that were not anticipat...
Basic performance measures for classification models are presented along with general selection guid...
<p>Classification performance measures of final prediction model at different risk thresholds.</p
Binary classification is one of the most frequent studies in applied machine learning problems in va...
Traditional measures for assessing the performance of classification models for binary outcomes are ...
Abstract. Performance metrics are used in various stages of the process aimed at solving a classific...
The performance of prediction models can be assessed using a variety of methods and metrics. Traditi...
textabstractThe performance of prediction models can be assessed using a variety of methods and metr...
<p>This indicates the extent that each metric can be used to predict performance.</p
<p>The performance of different classifiers associated with the attribute selection methods assessed...
This data provides the detailed test results of the benchmarking for the binary-classification perfo...
(a) Overall performance metrics (Precision, Recall, F(0.5) score and MCC) depending on the probabili...
International audienceThe selection of the best classification algorithm for a given dataset is a ve...
The area under the ROC (Receiver Operating Characteristic) curve, or simply AUC, has been widely u...
Prediction models are becoming more and more important in medicine and cardiology. Nowadays, specifi...
Classifiers that are deployed in the field can be used and evaluated in ways that were not anticipat...
Basic performance measures for classification models are presented along with general selection guid...
<p>Classification performance measures of final prediction model at different risk thresholds.</p
Binary classification is one of the most frequent studies in applied machine learning problems in va...
Traditional measures for assessing the performance of classification models for binary outcomes are ...
Abstract. Performance metrics are used in various stages of the process aimed at solving a classific...
The performance of prediction models can be assessed using a variety of methods and metrics. Traditi...
textabstractThe performance of prediction models can be assessed using a variety of methods and metr...
<p>This indicates the extent that each metric can be used to predict performance.</p
<p>The performance of different classifiers associated with the attribute selection methods assessed...
This data provides the detailed test results of the benchmarking for the binary-classification perfo...
(a) Overall performance metrics (Precision, Recall, F(0.5) score and MCC) depending on the probabili...
International audienceThe selection of the best classification algorithm for a given dataset is a ve...
The area under the ROC (Receiver Operating Characteristic) curve, or simply AUC, has been widely u...
Prediction models are becoming more and more important in medicine and cardiology. Nowadays, specifi...
Classifiers that are deployed in the field can be used and evaluated in ways that were not anticipat...
Add to ORKG