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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The proportion of actual negatives that are correctly identified
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The closeness of two or more measurements
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The total number of correct predictions made out of all the predictions
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The proportion of actual positives that are correctly identified
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Detailed explanation-1: -Accuracy is a proportional measure of the number of correct predictions over all predictions. Correct predictions are composed of true positives (TP) and true negatives (TN). All predictions are composed of the entirety of positive (P) and negative (N) examples.
Detailed explanation-2: -Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions.
Detailed explanation-3: -Classification Accuracy It is the ratio of number of correct predictions to the total number of input samples. It works well only if there are equal number of samples belonging to each class. For example, consider that there are 98% samples of class A and 2% samples of class B in our training set.