EMERGING TRENDS IN COMPUTING
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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defined as the fraction of positive cases that are correctly identified.
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defined as the percentage of true positive cases versus all the cases where the prediction is true.
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defined as the percentage of correct predictions out of all the observations.
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comparison between the prediction and reality
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Detailed explanation-1: -Precision: Precision is defined as the percentage of true positive cases versus all the cases where the prediction is true. Recall: It is defined as the fraction of positive cases that are correctly identified. Accuracy= 0.9% Precision=0.9375% Recall=0.9375% F1 Score=0.
Detailed explanation-2: -Recall. In the recall method, the fraction of positive cases that are correctly identified will be taken into consideration. It majorly takes into account the true reality cases wherein Reality there was a fire but the machine either detected it correctly or it didn’t.
Detailed explanation-3: -Recall, also known as the true positive rate (TPR), is the percentage of data samples that a machine learning model correctly identifies as belonging to a class of interest-the “positive class”-out of the total samples for that class.