Confusion matrix

Confusion matrix developed for each ih.iL mi-.i, and operator action,... 

The confusion matrix (NSAC-60) is a method that identifies potential operator errors lemming from incorrect diagnosis of an event. It can be used to identify the potential for an operator to conclude that a small LOCA has occurred, when it is actually a steam line break. This provides a method for identifying a wrong operator response to an off-normal plant condition. It is particularly useful in Step 5 of the. SHARP procedures, Documentation requirements are presented in Table 4.5-2. 

Classification Assignment Fig. 3. Confusion matrix for the HDPCA analysis of four spessartine garnets from different locations worldwide. The nth row and column correspond to the nth spessartine type truth and HDPCA statistical assignment, respectively. 

Table 1. Confusion matrix for the PLS-DA analysis of the California obsidian localities using 600 broadband LIBS spectra and 15 latent variables to produce 5 model classes. Horizontal lines separate the groupings for the Coso Volcanic Field by Draucker (2007) and the other four California obsidian localities...

Table 2. Confusion matrix for a test of the PLS-DA model for the Coso obsidian locality. The remaining 575 broadband LIBS spectra for Coso (not included in the original model) were assigned with nearly perfect locality classification (99.8%). Only Coso sample 189 from South Sugarloaf had some misclassification with Shoshone sample 173.

Confusion matrix for the 17 honey samples in the testing set analysed with the ANN... 

To judge the performance of the discriminant functions and the classification procedure in respect of future samples one can calculate misclassification probabilities or error rates. But these probabilities cannot be calculated in general because they depend on the unknown density functions of the classes. Instead we can usually utilize a measure called apparent error rate. The value of this quantity is easily calculated from the classification or confusion matrix based on the samples of the training set. For example with two classes we can have the following matrix ... 

Table 7.4 Confusion Matrix from an Ensemble of 100 Trained Neural Networks Predicting Gene Family Target Activity...

All the classification parameters can be derived from the confusion matrix, where the rows represent the known true classes and the columns the classes assigned by the classification method. It is a non-symmetric matrix of size GxG, where G is the number of classes. For example, for a three-class problem (G = 3) ... 

From the confusion matrix entries, the following parameters are defined ... 

Table 2 Use of the contingency table, or confusion matrix, of classification results (a). Ey is the number of objects from group i classified as j, Mi is the number of objects actually in group i, and is the number classified...

Tables Discriminant scores using the quadratic discriminant function as classifier (a), and the resulting confusion matrix (b)...

Complete linkage clustering, 106 Conditional probability, 127, 129 Confusion matrix, 127 Contingency table, 127 Convolution, 38 Cophenetic values, 98 Correlation coefficient, 17, 18,69,96, 176... 

In athree-class problem (C = 3) of30 objects (n = 30), after the application of a classification algorithm, the following confusion matrix is obtained ... 

A confusion matrix is a convenient way of summarizing error patterns. This is a matrix whose rows are the members of a stimulus set and whose columns are the possible responses to those stimuli (which, ordinarily, are the same objects as the stimuli) each cell of the matrix contains the frequency with which the column s response was given to the row s stimulus (i. e., the "confusability" of the presented stimulus and the resulting response). With perfect recognition, only the cells on the main diagonal are filled, if the... 

Table 3.3 The confusion matrix of the results of a discriminant analysis of the monoamine oxidase inhibitor dataset. ...

Some endpoints cannot be measured on a continuous scale, or for scientific or regulatory purposes can only be divided into two or more qualitative classes, for instance active/inactive. Goodness of fit can be described by the Cooper statistics, introduced to assess the significance of carcinogenicity tests.The goodness of fit parameters for a 2-level classification are shown in Table 9.3. This table is often, and very aptly, entitled a confusion matrix. 

With these parameters it can be built the called confusion matrix for class A ... 

The results of categorical classification are represented in the confusion matrix, which contains the numbers of correct classified objects in each class on the main diagonal and the misclassified objects in the off-diagonal. 

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