<p>(A) n<sub>1</sub> = n<sub>2</sub> = 25, <i>k</i> = 6. (B) n<sub>1</sub> = n<sub>2</sub> = 25, <i>k</i> = 9. (C) n<sub>1</sub> = n<sub>2</sub> = 25, <i>k</i> = 14. (D) n<sub>1</sub> = n<sub>2</sub> = 50, <i>k</i> = 6. (E) n<sub>1</sub> = n<sub>2</sub> = 50, <i>k</i> = 9. (F) n<sub>1</sub> = n<sub>2</sub> = 50, <i>k</i> = 15. The x-axis is FPR, and ...
<p>Receiver-operator-characteristic (ROC) curves are shown both in a per-pixel (<b>A</b>) and a per-...
To get back to a question asked after the last course (still on non-life insurance), I will spend so...
LogReg: logistic regression analysis. NB: Naive Bayes classifier. RF: random forest. SVM: support ve...
<p>(A) n<sub>1</sub> = n<sub>2</sub> = 25, ...
<p>(A) <i>n</i><sub>1</sub> = <i>n</i><sub>2</sub> = 25, μ = 2, <i>k</i> = 3. (B) <i>n</i><sub>1</su...
<p>(A) <i>n</i><sub>1</sub> = <i>n</i><sub>2</sub> = 25, μ = 2, <i>k</i> = 3. (B) <i>n</i><sub>1</su...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
<p>(a, b) are for the standard normal distribution; (c, d) are for the t-distribution with degrees o...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>ROC curves derived from the univariate logistic analysis corresponding to total cfDNA (AUC = 0.85...
<p>ROC curve of our method (Becker <i>et al.</i>) and three freely downloadable predictors: DISPRED2...
ROC curves of the feature group AB using Random undersampling and One-SVM-US techniques on the datas...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>(A) Over expression formula applied to simulated under expressed ...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>Receiver-operator-characteristic (ROC) curves are shown both in a per-pixel (<b>A</b>) and a per-...
To get back to a question asked after the last course (still on non-life insurance), I will spend so...
LogReg: logistic regression analysis. NB: Naive Bayes classifier. RF: random forest. SVM: support ve...
<p>(A) n<sub>1</sub> = n<sub>2</sub> = 25, ...
<p>(A) <i>n</i><sub>1</sub> = <i>n</i><sub>2</sub> = 25, μ = 2, <i>k</i> = 3. (B) <i>n</i><sub>1</su...
<p>(A) <i>n</i><sub>1</sub> = <i>n</i><sub>2</sub> = 25, μ = 2, <i>k</i> = 3. (B) <i>n</i><sub>1</su...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
<p>(a, b) are for the standard normal distribution; (c, d) are for the t-distribution with degrees o...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>ROC curves derived from the univariate logistic analysis corresponding to total cfDNA (AUC = 0.85...
<p>ROC curve of our method (Becker <i>et al.</i>) and three freely downloadable predictors: DISPRED2...
ROC curves of the feature group AB using Random undersampling and One-SVM-US techniques on the datas...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>(A) Over expression formula applied to simulated under expressed ...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>Receiver-operator-characteristic (ROC) curves are shown both in a per-pixel (<b>A</b>) and a per-...
To get back to a question asked after the last course (still on non-life insurance), I will spend so...
LogReg: logistic regression analysis. NB: Naive Bayes classifier. RF: random forest. SVM: support ve...