3_2_Model_Selection_Comparison_Matrix.xlsx

	A	B	C	D	E	F	G
1	Model Selection Comparison Matrix
2	Project: [Enter Project Name]
3	Date: [Enter Date]
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5	Evaluation Criteria	Weight (%)	Logistic Regression	Random Forest	XGBoost	Neural Network	Baseline
6	PERFORMANCE METRICS
7	Prediction Accuracy	25	4	5	5	3	2
8	Robustness to Data Quality	10	4	5	5	3	2
9	Performance Stability	10	4	5	5	3	2
10	OPERATIONAL REQUIREMENTS
11	Inference Latency	15	4	5	5	3	2
12	Scalability	10	4	5	5	3	2
13	Resource Efficiency	5	4	5	5	3	2
14	INTERPRETABILITY
15	Model Transparency	10	4	5	5	3	2
16	Explanation Quality	5	4	5	5	3	2
17	DEVELOPMENT & MAINTENANCE
18	Implementation Complexity	5	4	5	5	3	2
19	Maintenance Burden	5	4	5	5	3	2
20	WEIGHTED TOTAL SCORE	100	3.00	3.75	3.75	2.25	1.50
21	RANK		3	1	1	4	5
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23	Instructions:
24	1. Enter your project name and date at the top
25	2. Modify model names in row 5 to match your candidates
26	3. Adjust criteria and weights to fit your requirements (weights must sum to 100)
27	4. Score each model on each criterion using a 1-5 scale
28	5. Review weighted totals and ranks automatically calculated
29	6. Color coding: Red (poor) → Yellow (acceptable) → Green (excellent)
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