<p>TP = true positive; FP = false positive; FN = false negative; TN = true negative.</p
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
<p>The confusion matrix of multi-scale segmentation and supervised classification result.</p
++<p>, <sup>−+</sup>, <sup>−−</sup>, <sup>+−</sup> denote true positive, false positive, false negat...
<p>TP: true positives, FP: false positives, FN: false negatives, TN: ...
<p><i>TP</i> is the number of correct predictions that an instance is positive (<i>true positive</i>...
<p>Here, TP (true positive) and TN (true negative) denote the number of correctly predicted overlap ...
<p>(A) The left oval shows two actual labels: positives (P; blue; top half) and negatives (N; red; b...
<p> denotes the set of all possible (directed) edges among nodes in the network. FN = false negati...
<p>Confusion matrix of the result of the support vector machine classifier for the diagnosis.</p
Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p
<p>Confusion matrix of a classifier based on the gold standard of class labels.</p
<p>Confusion matrix for the classification of mixtures obtained by RF for the test set of partition ...
<p>Distribution of the test patients into false/true positives/negatives in a 10-fold CV test. Summ...
The top-left represents the TN, the top-right represents FP, the bottom-left is FN and the bottom-ri...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
<p>The confusion matrix of multi-scale segmentation and supervised classification result.</p
++<p>, <sup>−+</sup>, <sup>−−</sup>, <sup>+−</sup> denote true positive, false positive, false negat...
<p>TP: true positives, FP: false positives, FN: false negatives, TN: ...
<p><i>TP</i> is the number of correct predictions that an instance is positive (<i>true positive</i>...
<p>Here, TP (true positive) and TN (true negative) denote the number of correctly predicted overlap ...
<p>(A) The left oval shows two actual labels: positives (P; blue; top half) and negatives (N; red; b...
<p> denotes the set of all possible (directed) edges among nodes in the network. FN = false negati...
<p>Confusion matrix of the result of the support vector machine classifier for the diagnosis.</p
Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p
<p>Confusion matrix of a classifier based on the gold standard of class labels.</p
<p>Confusion matrix for the classification of mixtures obtained by RF for the test set of partition ...
<p>Distribution of the test patients into false/true positives/negatives in a 10-fold CV test. Summ...
The top-left represents the TN, the top-right represents FP, the bottom-left is FN and the bottom-ri...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
<p>The confusion matrix of multi-scale segmentation and supervised classification result.</p
++<p>, <sup>−+</sup>, <sup>−−</sup>, <sup>+−</sup> denote true positive, false positive, false negat...
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