With the help of a confusion matrix, we can compute-(1 Point)RecallPrecisionAccuracyAll of the above
Question
With the help of a confusion matrix, we can compute-
- Recall
- Precision
- Accuracy
- All of the above
Solution
All of the above. A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. It allows the visualization of the performance of an algorithm.
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Accuracy: It is the ratio of the sum of true positive and true negative to the total population. It measures the proportion of correct predictions (both positive and negative) made by the model.
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Precision: It is the ratio of true positive to the sum of true positive and false positive. It measures the proportion of positive identifications that were actually correct.
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Recall: It is the ratio of true positive to the sum of true positive and false negative. It measures the proportion of actual positives that were identified correctly.
So, with the help of a confusion matrix, we can compute Recall, Precision, and Accuracy.
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