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Assignment3AmalAlmansouriGEN101.xlsx

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sheet1

	Name	ID	Major					TP	FP	TN	FN
	Amal Almansoori	1078176	Biomedical Engineering
								9	11	0	0								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	Benign	Benign	1	0	11	8	1.00	0.11	0.00	1.00	0.60	0.15
Introductory Artificial Intelligence	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.			2	0.87	Malignant	Benign	1	1	10	8	0.50	0.11	0.09	0.91	0.55	0.15
Assignment No				3	0.78	Benign	Benign	2	1	10	7	0.67	0.22	0.09	0.91	0.60	0.27
3				4	0.98	Malignant	Benign	2	2	9	7	0.50	0.22	0.18	0.82	0.55	0.27
Assignment Title				5	0.36	Benign	Benign	3	3	8	6	0.50	0.33	0.27	0.73	0.55	0.35
Performance Evaluation				6	0.12	Malignant	Benign	3	3	8	6	0.50	0.33	0.27	0.73	0.55	0.35
Instructors				7	0.85	Benign	Benign	4	3	8	5	0.57	0.44	0.27	0.73	0.60	0.42
Prof. Mohammed Ghazal				8	0.87	Malignant	Benign	4	4	7	5	0.50	0.44	0.36	0.64	0.55	0.42
Eng. Maha Yaghi				9	0.69	Malignant	Benign	4	5	6	5	0.44	0.44	0.45	0.55	0.50	0.42
Eng. Malaz Osman				10	0.83	Malignant	Benign	4	6	5	5	0.40	0.44	0.55	0.45	0.45	0.42
Eng. Marah AlHalabi				11	0.90	Benign	Benign	6	6	5	3	0.50	0.67	0.55	0.45	0.55	0.52
Eng. Tasnim Basmaji				12	0.71	Benign	Benign	6	6	5	3	0.50	0.67	0.55	0.45	0.55	0.52
Eng. Yasmin Abu-Haeyeh				13	0.72	Malignant	Benign	6	8	3	3	0.43	0.67	0.73	0.27	0.45	0.52
				14	0.93	Malignant	Benign	6	8	3	3	0.43	0.67	0.73	0.27	0.45	0.52
				15	0.94	Malignant	Benign	6	9	2	3	0.40	0.67	0.82	0.18	0.40	0.52
				16	0.34	Malignant	Benign	6	10	1	3	0.38	0.67	0.91	0.09	0.35	0.52
				17	0.71	Benign	Benign	7	10	1	2	0.41	0.78	0.91	0.09	0.40	0.56		Receiver Operating Characteristics Curve
				18	0.69	Benign	Benign	8	10	1	1	0.44	0.89	0.91	0.09	0.45	0.59
				19	0.32	Benign	Benign	9	10	1	0	0.47	1.00	0.91	0.09	0.50	0.62					+	-
				20	0.35	Malignant	Benign	9	11	0	0	0.45	1.00	1.00	0.00	0.45	0.62	AUC		Select Your Major	Welcome to Assignment 3. Please follow the instructions below to start working on your assignment.
																		0.5205		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).

ROC

0 9.0909090909090912E-2 9.0909090909090912E-2 0.18181818181818182 0.27272727272727271 0.27272727272727271 0.27272727272727271 0.36363636363636365 0.45454545454545453 0.54545454545454541 0.54545454545454541 0.54545454545454541 0.72727272727272729 0.72727272727272729 0.81818181818181823 0.90909090909090906 0.90909090909090906 0.90909090909090906 0.90909090909090906 1 0.1111111111111111 0.1111111111111111 0.22222222222222221 0.22222222222222221 0.33333333333333331 0.33333333333333331 0.44444444444444442 0.44444444444444442 0.44444444444444442 0.44444444444444442 0.66666666 666666663 0.66666666666666663 0.66666666666666663 0.66666666666666663 0.66666666666666663 0.66666666666666663 0.77777777777777779 0.88888888888888884 1 1

FPR

TPR

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.

Sheet3

				TP	FP	TN	FN
				9	11	0	0
Instance	P(+\|A)	True Class (Human)	Predicited (AI)	TP	FP	TN	FN	Precision	Recall (Sensitivity) TPR	FPR	Specificity/TNR	ACC	F1
1	0.98	Benign	Benign	1	0	11	8	1.00	0.11	0.00	1.00	0.60	0.15
2	0.94	Malignant	Benign	1	1	10	8	0.50	0.11	0.09	0.91	0.55	0.15
3	0.93	Benign	Benign	2	1	10	7	0.67	0.22	0.09	0.91	0.60	0.27
4	0.90	Malignant	Benign	2	2	9	7	0.50	0.22	0.18	0.82	0.55	0.27
5	0.87	Benign	Benign	3	3	8	6	0.50	0.33	0.27	0.73	0.55	0.35
6	0.87	Malignant	Benign	3	3	8	6	0.50	0.33	0.27	0.73	0.55	0.35
7	0.85	Benign	Benign	4	3	8	5	0.57	0.44	0.27	0.73	0.60	0.42
8	0.83	Malignant	Benign	4	4	7	5	0.50	0.44	0.36	0.64	0.55	0.42
9	0.78	Malignant	Benign	4	5	6	5	0.44	0.44	0.45	0.55	0.50	0.42
10	0.72	Malignant	Benign	4	6	5	5	0.40	0.44	0.55	0.45	0.45	0.42
11	0.71	Benign	Benign	6	6	5	3	0.50	0.67	0.55	0.45	0.55	0.52
12	0.71	Benign	Benign	6	6	5	3	0.50	0.67	0.55	0.45	0.55	0.52
13	0.69	Malignant	Benign	6	8	3	3	0.43	0.67	0.73	0.27	0.45	0.52
14	0.69	Malignant	Benign	6	8	3	3	0.43	0.67	0.73	0.27	0.45	0.52
15	0.36	Malignant	Benign	6	9	2	3	0.40	0.67	0.82	0.18	0.40	0.52
16	0.35	Malignant	Benign	6	10	1	3	0.38	0.67	0.91	0.09	0.35	0.52
17	0.34	Benign	Benign	7	10	1	2	0.41	0.78	0.91	0.09	0.40	0.56
18	0.32	Benign	Benign	8	10	1	1	0.44	0.89	0.91	0.09	0.45	0.59
19	0.12	Benign	Benign	9	10	1	0	0.47	1.00	0.91	0.09	0.50	0.62
20	0.10	Malignant	Benign	9	11	0	0	0.45	1.00	1.00	0.00	0.45	0.62
			Model 1
			A1	0.01
			A2	0.04
			A3	0.025
			A4	0.1215
			A5	0.234
			A6	0.09
			AUC	0.5205

Precision vs Recall Curve

0.1111111111111111 0.1111111111111111 0.22222222222222221 0.22222222222222221 0.33333333333333331 0.33333333333333331 0.44444444444444442 0.44444444444444442 0.44444444444444442 0.44444444444444442 0.66666666666666663 0.66666666666666663 0.66666666666666663 0.66666666666666663 0.66666666666666663 0.66666666666666663 0.77777777777777779 0.88888888888888884 1 1 1 0.5 0.66666666666666663 0.5 0.5 0.5 0.5714285714285714 0.5 0.44444444444444442 0.4 0.5 0.5 0.42857142857142855 0.42857142857142855 0.4 0.375 0.41176470588235292 0.44444444444444442 0.47368421052631576 0.45

Recall

Precision

ROC

FPR

TPR