Which of the following evaluation metrics can be used to evaluate a model with categorical output variable with more than two categories? (check tree best answers) ☐ sensitivity (true positive rate, recall) deviance cross-entropy O accuracy (proportion of correctly predicted outputs) AUC and ROC root mean squared error ☐ specificity (true negative rate) ☐ false positive rate
Which of the following evaluation metrics can be used to evaluate a model with categorical output variable with more than two categories? (check tree best answers) ☐ sensitivity (true positive rate, recall) deviance cross-entropy O accuracy (proportion of correctly predicted outputs) AUC and ROC root mean squared error ☐ specificity (true negative rate) ☐ false positive rate
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Transcribed Image Text:Which of the following evaluation metrics can be used to evaluate a model with categorical
output variable with more than two categories? (check tree best answers)
☐ sensitivity (true positive rate, recall)
deviance
cross-entropy
O accuracy (proportion of correctly predicted outputs)
AUC and ROC
root mean squared error
☐ specificity (true negative rate)
☐ false positive rate
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