excel question
sheet1
| Name | ID | Major | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Rawan salem bajjabaa | 1076676 | Architecture | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precision-Recall Curve | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course Code | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| GEN101 | Instance | P(+|A) | True Class (Human) | Predicited (AI) | TP | FP | TN | FN | Precision | Recall (Sensitivity) TPR | FPR | Specificity/TNR | ACC | F1 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course Name | 1 | 0.10 | Cape_code | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Introductory Artificial Intelligence | Given the following test set of 20 architectural styles (Cape_code(+) and Art_deco(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | 2 | 0.87 | Art_deco | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Assignment No | 3 | 0.78 | Cape_code | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 3 | 4 | 0.98 | Art_deco | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Assignment Title | 5 | 0.36 | Cape_code | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Performance Evaluation | 6 | 0.12 | Art_deco | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Instructors | 7 | 0.85 | Cape_code | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prof. Mohammed Ghazal | 8 | 0.87 | Art_deco | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Eng. Maha Yaghi | 9 | 0.69 | Art_deco | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Eng. Malaz Osman | 10 | 0.83 | Art_deco | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Eng. Marah AlHalabi | 11 | 0.90 | Cape_code | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Eng. Tasnim Basmaji | 12 | 0.71 | Cape_code | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Eng. Yasmin Abu-Haeyeh | 13 | 0.72 | Art_deco | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 14 | 0.93 | Art_deco | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 15 | 0.94 | Art_deco | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 16 | 0.34 | Art_deco | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 17 | 0.71 | Cape_code | Receiver Operating Characteristics Curve | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 18 | 0.69 | Cape_code | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 19 | 0.32 | Cape_code | + | - | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 20 | 0.35 | Art_deco | AUC | Select Your Major | Welcome to Assignment 3. Please follow the instructions below to start working on your assignment. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Architecture | Given the following test set of 20 architectural styles (Cape_code(+) and Art_deco(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Cape_code | Art_deco | 0 | 0.25 | 0.36 | 0.13 | 0.35 | 0.36 | 0.12 | 0.32 | 0.38 | 0.36 | 0.72 | 0.75 | 0.93 | 0.67 | 0.30 | 0.86 | 0.46 | 0.75 | 0.91 | 0.72 | 0.36 | + | - | + | - | + | - | + | + | + | - | - | + | - | - | + | - | - | - | + | + | |||||||||||||||||||||||||||||
| Aviation | Given the following test set of 20 airplane failure reasons (Overload(+) and Design_flaw(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Overload | Design_flaw | 1 | 0.57 | 0.90 | 0.32 | 0.75 | 0.53 | 0.32 | 0.85 | 0.76 | 0.34 | 0.77 | 0.73 | 0.61 | 0.77 | 0.21 | 0.73 | 0.45 | 0.93 | 0.24 | 0.28 | 0.99 | + | - | + | + | + | + | + | + | - | - | - | + | - | - | + | + | + | - | - | - | |||||||||||||||||||||||||||||
| Biomedical Engineering | Given the following test set of 20 cancer grades (Benign(+) and Malignant(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Benign | Malignant | 2 | 0.81 | 0.83 | 0.20 | 0.82 | 0.80 | 0.65 | 0.72 | 0.43 | 0.70 | 0.72 | 0.85 | 0.36 | 0.33 | 0.41 | 0.74 | 0.36 | 0.45 | 0.75 | 0.48 | 0.53 | + | + | - | - | - | + | + | - | + | - | - | + | + | + | - | + | - | + | - | - | |||||||||||||||||||||||||||||
| Business | Given the following test set of 20 bank accounts (Investment(+) and Saving(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Investment | Saving | 3 | 0.99 | 0.84 | 0.96 | 0.85 | 0.90 | 0.13 | 0.75 | 0.86 | 0.82 | 0.88 | 0.59 | 0.67 | 0.99 | 0.95 | 0.68 | 0.73 | 0.11 | 0.64 | 0.31 | 0.49 | + | + | + | - | + | - | - | + | + | + | - | + | - | + | - | + | - | + | - | - | |||||||||||||||||||||||||||||
| Chemical Engineering | Given the following test set of 20 gas sensor responses (Linear(+) and Exponential(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Linear | Exponential | 4 | 0.45 | 0.42 | 0.79 | 1.00 | 0.72 | 0.84 | 0.48 | 0.54 | 0.90 | 0.55 | 0.54 | 0.52 | 0.10 | 0.64 | 0.66 | 0.31 | 0.13 | 0.79 | 0.96 | 0.56 | - | - | + | - | - | - | + | + | + | + | + | - | + | - | - | + | - | + | - | - | |||||||||||||||||||||||||||||
| Civil Engineering | Given the following test set of 20 house structure conditions (Strong(+) and Weak(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Strong | Weak | 5 | 0.75 | 0.94 | 0.37 | 0.95 | 0.15 | 0.53 | 0.54 | 0.42 | 0.77 | 0.54 | 0.60 | 0.15 | 0.73 | 0.30 | 0.97 | 0.48 | 0.74 | 0.76 | 0.29 | 0.55 | - | - | + | - | + | - | - | + | + | - | - | - | - | + | - | + | - | + | + | + | |||||||||||||||||||||||||||||
| Computer Engineering | Given the following test set of 20 CPU performances (Super(+) and Poor(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Super | Poor | 6 | 0.10 | 0.87 | 0.78 | 0.98 | 0.36 | 0.12 | 0.85 | 0.87 | 0.69 | 0.83 | 0.90 | 0.71 | 0.72 | 0.93 | 0.94 | 0.34 | 0.71 | 0.69 | 0.32 | 0.35 | + | - | + | - | + | - | + | - | - | - | + | + | - | - | - | - | + | + | + | - | |||||||||||||||||||||||||||||
| Cybersecurity | Given the following test set of 20 CPU performances (Super(+) and Poor(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Super | Poor | 7 | 0.47 | 0.35 | 0.99 | 0.18 | 0.85 | 0.19 | 0.72 | 0.58 | 0.84 | 0.72 | 0.18 | 0.60 | 0.50 | 0.51 | 0.34 | 0.86 | 0.91 | 0.72 | 0.76 | 0.75 | - | + | - | + | + | - | + | + | - | + | - | - | + | + | - | + | + | - | - | + | |||||||||||||||||||||||||||||
| Electrical Engineering | Given the following test set of 20 power outage types (Distribution(+) and Transmission(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Distribution | Transmission | 8 | 0.53 | 0.39 | 0.91 | 0.60 | 0.70 | 0.73 | 0.53 | 0.12 | 0.37 | 0.14 | 0.21 | 0.63 | 0.20 | 0.75 | 0.36 | 0.89 | 0.55 | 0.41 | 0.81 | 0.66 | - | - | + | - | - | - | - | + | - | + | + | - | - | - | + | + | - | - | + | - | |||||||||||||||||||||||||||||
| HR | Given the following test set of 20 promotion eligibilities (Eligible(+) and Not_eligible(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Eligible | Not_Eligible | 9 | 0.23 | 0.49 | 0.55 | 0.44 | 0.73 | 0.76 | 0.16 | 0.25 | 0.54 | 0.79 | 0.79 | 0.19 | 0.49 | 0.12 | 0.72 | 0.20 | 0.77 | 0.54 | 0.66 | 0.18 | - | + | + | + | - | + | - | - | + | + | - | + | + | + | + | - | - | - | + | + | |||||||||||||||||||||||||||||
| Industrial Engineering | Given the following test set of 20 steel plates faults (Stains(+) and Bumps(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Stains | Bumps | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Information Technology | Given the following test set of 20 CPU performances (Super(+) and Poor(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Super | Poor | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Interior Design | Given the following test set of 20 home decors (Modern(+) and Traditional(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Modern | Traditional | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Mechanical Engineering | Given the following test set of 20 gear conditions (Normal(+) and Damaged(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Normal | Damaged | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Software Engineering | Given the following test set of 20 CPU performances (Super(+) and Poor(-)). Calculate the different performance measures (precision, recall, accuracy, …). Construct the ROC and then calculate the AUC. | Super | Poor |
1. Insert your name and student ID under Name and ID. 2. Select your major from the drop-down list. This will generate a unique set of data for you only. For a successful completion of the assignment, you need to fill all bordered White cells. Note that any similarity detected will be reported to the Office of Academic Intergrity (OAI).
3. Apply threshold at each unique value of P(+|A) to find the predicted class (Predicted (AI)) for each instance. Note: Make sure you do not change the data in the colored cells. Hint: You may copy and paste the data (by value) to another sheet then sort it and begin working on the performance evaluation. Once done, you may fill in the bordered white cells in this sheet with your final answers. 4. Count the number of TP, FP, TN, and FN at each threshold and then calculate the different performance evaluation measures for each instance. The formulas can be found in the performance evaluation slides on blackboard.
5. Use the calculated values to plot the Precision-Recall curve and the Reciever Operating Characteristics (ROC) curve. 6. Find the area under the curve (AUC). 7. Once completed, save the file and submit it on blackboard.