Consider the same example of classifying comments as toxic or not toxic. For the sake of...

 

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Consider the same example of classifying comments as toxic or not toxic. For the sake of consistent terminology, let's label a toxic comment as a "positive" label in the terminology of binary classification. Suppose we trained a predictor and then when assessing our predictor on our validation data, counted the following scenarios: • There are 15 comments where our model predicted it was toxic, and the comment was actually toxic. • There are 20 comments where our model predicted it was toxic, but the comment was actually not toxic. • There are 30 comments where our model predicted it was not toxic, but the comment was actually toxic. • There are 5 comments where our model predicted it was not toxic, and the comment was actually not toxic. What is the False Negative Rate of this predictor? Write your answer as a number between 0 and 1, rounded to two decimal places (e.g., 0.25). Consider the same example of classifying comments as toxic or not toxic. For the sake of consistent terminology, let's label a toxic comment as a "positive" label in the terminology of binary classification. Suppose we trained a predictor and then when assessing our predictor on our validation data, counted the following scenarios: • There are 15 comments where our model predicted it was toxic, and the comment was actually toxic. • There are 20 comments where our model predicted it was toxic, but the comment was actually not toxic. • There are 30 comments where our model predicted it was not toxic, but the comment was actually toxic. • There are 5 comments where our model predicted it was not toxic, and the comment was actually not toxic. What is the False Negative Rate of this predictor? Write your answer as a number between 0 and 1, rounded to two decimal places (e.g., 0.25).

Answer rating: 100% (QA)

the Fake negative ovote the persporter of actual positive instances that are ... View the full answer