APPLICATION OF SUPERVISED LEARNING
ARTIFICIAL INTELLIGENCE
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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, 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.
Detailed explanation-3: -Weighted-F1 = (6 × 42.1% + 10 × 30.8% + 9 × 66.7%) / 25 = 46.4% Weighted-precision=(6 × 30.8% + 10 × 66.7% + 9 × 66.7%)/25 = 58.1% Weighted-recall = (6 × 66.7% + 10 × 20.0% + 9 × 66.7%) / 25 = 48.0%