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.

1. 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.

2. 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.

3. 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.

Question

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.

1. 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.

2. 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.

3. 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.

Knowee AI · Accepted Answer

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.

1. 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.

2. 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.

3. 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.

With the help of a confusion matrix, we can compute-(1 Point)RecallPrecisionAccuracyAll of the above

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With the help of a confusion matrix, we can compute-

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