Confusion matrix (error matrix): the entry in row k and column l is the number of test datapoints which belong to class k and for which our CASIMAC predicts the class label l. Correct classifications (on the diagonal) are highlighted in bold. There are no misclassifications between members of the classes 1 and 2, as can be expected from Fig 9a.</p
<p>Row labels represent the true class and column labels represent the class predicted by the model....
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
The diagonal values represent the ratio of correct classifications for each word, and the off-diagon...
<p>Confusion matrix of a classifier based on the gold standard of class labels.</p
<p>The decoder was trained and tested on good exemplars (left column) and trained and tested on bad ...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
<p>The subfigures correspond to the performance of the algorithm using different numbers of labeled ...
<p>Fraction of the test data that is assigned to each class based on the posterior probability assig...
A confusion matrix of classified students using the model developed by the training dataset.</p
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
A confusion matrix of classified students using the model developed by the training dataset in each ...
<p>Row labels represent the true class and column labels represent the class predicted by the model....
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
The diagonal values represent the ratio of correct classifications for each word, and the off-diagon...
<p>Confusion matrix of a classifier based on the gold standard of class labels.</p
<p>The decoder was trained and tested on good exemplars (left column) and trained and tested on bad ...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
<p>The subfigures correspond to the performance of the algorithm using different numbers of labeled ...
<p>Fraction of the test data that is assigned to each class based on the posterior probability assig...
A confusion matrix of classified students using the model developed by the training dataset.</p
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
A confusion matrix of classified students using the model developed by the training dataset in each ...
<p>Row labels represent the true class and column labels represent the class predicted by the model....
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
The confusion matrix representing the computed classification accuracy % for the proposed research w...