False positive rate confusion matrix
[DOCX File]Department of Computer Science - Old Dominion University
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It is commonly also called sensitivity, and corresponds to the true positive rate. It is defined by the formula: Recall = Sensitivity = tp/(tp+fn) where tp and fn are the numbers of true positive and false negative predictions for the considered class. tp + fn is the total number of test examples of the considered class.
[DOC File]Introduction - National Sun Yat-sen University
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For all three models above, the False Positive Rate (FPR), the fraction of negative examples predicted as a positive class, is rather small (0.001), which means it is very unlikely to incorrectly predict a non-winner player as a winner. ... We could see from the confusion matrix that the resulting decision tree classified 92 known Cy Young ...
[DOCX File]The University of Tennessee at Chattanooga
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For the validation data construct the confusion matrix and calculate the Accuracy rate, Misclassification rate, Sensitivity, Specificity and False Positive rate. Place a SAS Code node (look for it in Utility tab) in the diagram and connect all the nodes without a successor node to it.
[DOC File]A Machine Learning Approach to Object Recognition in the ...
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Each added stage to the classifier tends to reduce the false positive rate, but also reduces the detection rate [3]. ... cross-validation experiment with a multi-class SVM with polynomial kernel of 4th degree and the result is the following confusion matrix: predicted positive negative partial 240 0 10 positive actual 3 24 14 negative 26 3 192 ...
[DOC File]Chapter 10:
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The resulting confusion matrix is given in Table 11.5. The misclassification rate of 14.7% is significantly worse than the 2% achieved using discriminant analysis on the same data set. ACTUAL PREDICTED setosa versicolor virginica Class 1 (setosa) 50 0 0 Class 2 (versicolor) 0 46 18 Class 3 (virginica) 0 4 32
Logistic Regression - Daffodil International University
A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. Few terms to remember in the context of confusion matrix, refer the table below:
[DOC File]Iran University of Science and Technology
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In table 4, the performance of the fusion classification algorithm has been described by the obtained confusion matrix. For instance, the fifth row of this table shows that 2, 3, 1, 4, 0, 1 and 0 beat numbers were falsely classified into the Normal, LBBB, RBBB, APB, VE, PB and VF categories, respectively.
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