(c) Based on what we know about Observation B, if the cutoff value is lowered to 0.2, what happens to the values of sensitivity and specificity? Explain. Use the ROC curve to estimate the values of sensitivity and specificity for a cutoff value of 0.2. If required, round your answers to three decimal places. Do not round intermediate calculations.Answer Part C, there are no other parts here. I have the sensitivity already and is equal to 1. What is the specificity??If you are not 100% sure you are right, do not attempt this is frustrating. The cutoff value has changed from 0.5 to 0.2. The sensitivity is 1 which is correct. I need help with the specificity. Sensitivity does not equal 1/14 and specificity does not equal 1/31 so don't answer that and do not answer any other parts just answer part C. Fleur-de-Lis is a boutique bakery specializing in cupcakes. The bakers at Fleur-de-Lis like to experiment with different combinations of four major ingredients in its cupcakes and collect customer feedback; it has data on 150 combinations ofingredients with the corresponding customer reception for each combination classified as "thumbs up" (Class 1) or "thumbs down" (Class 0). To better anticipate the customer feedback of new recipes, Fleur-de-Lis has determined that a k-nearest neighbors classifier with k 510 seems to perform well.Using a cutoff value of 0.5 and a validation set of 45 observations, Fleur-de-Lis constructs following confusion matrix and the ROC curve for the k-nearest neighbors classifier with k 510:Predicted FeedbackActual FeedbackThumbs UpThumbs DownThumbs Up131Thumbs Down301.00.90.80.70.60.50.40.3• Random Classifier• Optimum Classifier• Fitted Classifier0.20.1 -0.00.00.10.2 0.30.4 0.50.60.70.80.91.01-specificityAs the confusion matrix shows, there is one observation that actually received thumbs down, but the k-nearest neighbors classifier predicts a thumbs up. Also, there is one observation that actually received a thumbs up, but the k-nearestneighbors classifier predicts a thumbs down. Specifically:Probability ofThumbs UpObservation IDActual ClassPredicted ClassAThumbs Down0.5Thumbs UpВThumbs Up0.2Thumbs DownSensitivity (c) Based on what we know about Observation B, if the cutoff value is lowered to 0.2, what happens to the values of sensitivity and specificity? Explain. Use the ROC curve to estimate the values of sensitivity and specificity for a cutoff value of0.2.If required, round your answers to three decimal places. Do not round intermediate calculations.Sensitivity =1Specificity =0.032 8

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Answered: (c) Based on what we know about…

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(c) Based on what we know about Observation B, if the cutoff value is lowered to 0.2, what happens to the values of sensitivity and specificity? Explain. Use the ROC curve to estimate the values of sensitivity and specificity for a cutoff value of 0.2. If required, round your answers to three decimal places. Do not round intermediate calculations.

Answer Part C, there are no other parts here. I have the sensitivity already and is equal to 1. What is the specificity??
If you are not 100% sure you are right, do not attempt this is frustrating. The cutoff value has changed from 0.5 to 0.2. The sensitivity is 1 which is correct. I need help with the specificity. Sensitivity does not equal 1/14 and specificity does not equal 1/31 so don't answer that and do not answer any other parts just answer part C.

Fleur-de-Lis is a boutique bakery specializing in cupcakes. The bakers at Fleur-de-Lis like to experiment with different combinations of four major ingredients in its cupcakes and collect customer feedback; it has data on 150 combinations of
ingredients with the corresponding customer reception for each combination classified as "thumbs up" (Class 1) or "thumbs down" (Class 0). To better anticipate the customer feedback of new recipes, Fleur-de-Lis has determined that a k-
nearest neighbors classifier with k 510 seems to perform well.
Using a cutoff value of 0.5 and a validation set of 45 observations, Fleur-de-Lis constructs following confusion matrix and the ROC curve for the k-nearest neighbors classifier with k 510:
Predicted Feedback
Actual Feedback
Thumbs Up
Thumbs Down
Thumbs Up
13
1
Thumbs Down
30
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
• Random Classifier
• Optimum Classifier
• Fitted Classifier
0.2
0.1 -
0.0
0.0
0.1
0.2 0.3
0.4 0.5
0.6
0.7
0.8
0.9
1.0
1-specificity
As the confusion matrix shows, there is one observation that actually received thumbs down, but the k-nearest neighbors classifier predicts a thumbs up. Also, there is one observation that actually received a thumbs up, but the k-nearest
neighbors classifier predicts a thumbs down. Specifically:
Probability of
Thumbs Up
Observation ID
Actual Class
Predicted Class
A
Thumbs Down
0.5
Thumbs Up
В
Thumbs Up
0.2
Thumbs Down
Sensitivity

(c) Based on what we know about Observation B, if the cutoff value is lowered to 0.2, what happens to the values of sensitivity and specificity? Explain. Use the ROC curve to estimate the values of sensitivity and specificity for a cutoff value of
0.2.
If required, round your answers to three decimal places. Do not round intermediate calculations.
Sensitivity =
1
Specificity =
0.032 8

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