COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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FN
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TP
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TN
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FP
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Detailed explanation-1: -true negative is 0% whereas true positive is 100% correctly classified.
Detailed explanation-2: -Your logistic classification is only prediction one class (in this case class 0) and is not respecting any other outcome at all.
Detailed explanation-3: -A Type I error can also be considered a false positive, as you are falsely claiming that there is a statistically significant difference between the variables at hand when there, in fact, is not. A Type II error, on the contrary, occurs when you fail to reject the null hypothesis when you should have.
Detailed explanation-4: -False-positive(FP): FP is incorrect positive prediction means that the actual value was negative but the model predicted a positive value. It is also known as the Type 1 error.