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6. Calculate the following performance metrics for the confusion matrix shown below. * True positive rate....
Evaluating Predictive Performance ( Business Analytics) Confusion Matrix !!! A publisher plans to boost the sales...
Evaluating Predictive Performance ( Business Analytics) Confusion Matrix !!! A publisher plans to boost the sales of its most popular magazine by sending out promotional mails. We refer to a customer as a responder if he/she subscribes to the magazine for the next year after receiving a promotional mail. Otherwise the customer is referred to as a non-responder. Denote responder by C1 and non-responder by C2. The publisher has built a model to classify each customer as either a responder...
Performance Metrics: Which of the following are terms used for performance metrics a. Specificity & Precision...
Performance Metrics: Which of the following are terms used for performance metrics a. Specificity & Precision b. Precision & Recall c. Recall & Sensitivity d. band e All of the above 9. Performance Metrics: When looking at the ROC/AUC curve, what are the values being compared represented on the x-axis and y-axis? a. False Positive Rate and True Positive Rate b. Precision and True Positive Rate c. False Positive Rate and Precision d. True Positive Rate and Specificity e. None...
Confusion Matrix The following table summarize performance of a classification algorithm for identifying cancerous (Malignant) cells....
Confusion Matrix The following table summarize performance of a classification algorithm for identifying cancerous (Malignant) cells. Table-1: Outcome from a classifier Model M1 Prediction Benign Malignant Actual Benign 60 15 Malignant 5 35 Compute the accuracy and error rate of the classifier. Compute the sensitivity and specificity of the classifier. Compute the precision and recall of the classifier. Compute the F1 measure of the classifier. Compute the Fβ measure of the classifier in which weights recall twice as much as...
1. Given a binary classification problem (Class-1 or Class-0), draw a Confusion Matrix showing result counts...
1. Given a binary classification problem (Class-1 or Class-0), draw a Confusion Matrix showing result counts (fx) in terms of Predicted and Actual class. Provide calculations for Accuracy and Error Rate, highlighting False Positives (FP) and False Negatives (FN).
Following is a confusion matrix table formed from the patients undergoing COVID -19 test. Predicted Positive...
Following is a confusion matrix table formed from the patients undergoing COVID -19 test. Predicted Positive Negative Observed Positive 6 4 Negative 2 8 The precision of the model is 0.75 0.8 0.7 1
Based on the test set above, calculate the precision rate. Based on the test set above, calculate the recall rate. calculate the F1 measure. calculate the misclassification rate. 1-4. The table below...
Based on the test set above, calculate the precision rate.
Based on the test set above, calculate the recall rate.
calculate the F1 measure.
calculate the misclassification rate.
1-4. The table below shows the predictions made for a categorical target feature by a model for a test dataset. ID Target Prediction ID Target Prediction ID Target Prediction 8 true 1 false false 15 false false true 2 false 9 false false 16 false false false 3 false false 10 false...
Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559...
Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is...
Answer the following questions 3. (a) Consider the contingency table below and compute the ACCURACY rate...
Answer the following questions 3. (a) Consider the contingency table below and compute the ACCURACY rate and ERROR rate for model M1. MODEL M1 PREDICTED CLASS Class-Yes Class No ACTUAL Class Yes 10 70 CLASS Class No 10 10 (b) Consider the contingency table below and compute the ACCURACY rate and ERROR rate for model M2 MODEL M2 PREDICTED CLASS Class Yes Class-No ACTUAL Class-Yes 70 CLASS Class No 10 20 (c) Given the following cost matrix, compute the cost...
Giving the following confusion matrix from the evaluation process, what is the value for accuracy? Actual...
Giving the following confusion matrix from the evaluation process, what is the value for accuracy? Actual Positive Actual Negative Classified Positive 90 50 Classified Negative 50 10 O 0.64 O 0.5 O 0.1 O 0.9
Q1. An E.M.G. controller for a prosthetic hand is used to classify the users intentions as 'open the hand' clos...
Q1. An E.M.G. controller for a prosthetic hand is used to classify the users intentions as 'open the hand' close the hand' 'lock'. The following confusion matrix has emerged from tests. Predicted class open close lock TPR FNR open 47 2 True class | close | 1 | 42 | 7 18 27 PPR FDR where TPR= True Positive Rate FNR False Negative Rate PPV= Positive Predictive Value FDR= False Discovery Rate (Negative Predictive Value) Calculate as a percentage, TPR,...
Performance Metrics: Which of the following are terms used for performance metrics a. Specificity & Precision b. Precision & Recall c. Recall & Sensitivity d. band e All of the above 9. Performance Metrics: When looking at the ROC/AUC curve, what are the values being compared represented on the x-axis and y-axis? a. False Positive Rate and True Positive Rate b. Precision and True Positive Rate c. False Positive Rate and Precision d. True Positive Rate and Specificity e. None...
1. Given a binary classification problem (Class-1 or Class-0), draw a Confusion Matrix showing result counts (fx) in terms of Predicted and Actual class. Provide calculations for Accuracy and Error Rate, highlighting False Positives (FP) and False Negatives (FN).
Following is a confusion matrix table formed from the patients undergoing COVID -19 test. Predicted Positive Negative Observed Positive 6 4 Negative 2 8 The precision of the model is 0.75 0.8 0.7 1
Based on the test set above, calculate the precision rate.
Based on the test set above, calculate the recall rate.
calculate the F1 measure.
calculate the misclassification rate.
1-4. The table below shows the predictions made for a categorical target feature by a model for a test dataset. ID Target Prediction ID Target Prediction ID Target Prediction 8 true 1 false false 15 false false true 2 false 9 false false 16 false false false 3 false false 10 false...
Answer the following questions 3. (a) Consider the contingency table below and compute the ACCURACY rate and ERROR rate for model M1. MODEL M1 PREDICTED CLASS Class-Yes Class No ACTUAL Class Yes 10 70 CLASS Class No 10 10 (b) Consider the contingency table below and compute the ACCURACY rate and ERROR rate for model M2 MODEL M2 PREDICTED CLASS Class Yes Class-No ACTUAL Class-Yes 70 CLASS Class No 10 20 (c) Given the following cost matrix, compute the cost...
Giving the following confusion matrix from the evaluation process, what is the value for accuracy? Actual Positive Actual Negative Classified Positive 90 50 Classified Negative 50 10 O 0.64 O 0.5 O 0.1 O 0.9
Q1. An E.M.G. controller for a prosthetic hand is used to classify the users intentions as 'open the hand' close the hand' 'lock'. The following confusion matrix has emerged from tests. Predicted class open close lock TPR FNR open 47 2 True class | close | 1 | 42 | 7 18 27 PPR FDR where TPR= True Positive Rate FNR False Negative Rate PPV= Positive Predictive Value FDR= False Discovery Rate (Negative Predictive Value) Calculate as a percentage, TPR,...
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