Machine Learning Model Selection Framework
A Technical Guide to Choosing the Right ML Approach for Your Business Problem
22 Pages • Free PDF Download
22-page technical guide for ML model selection by problem type, data, performance, and ops constraints. Includes decision trees and benchmark matrices.
Key Findings
- 62% of ML failures trace back to inappropriate model selection
- XGBoost outperforms deep learning on 80%+ of tabular datasets (NeurIPS, 2024)
- Transfer learning reduces data requirements by 10-100x (Stanford AI Lab, 2025)
- Model complexity tax: 10-100x cost difference in inference
- SHAP explanations increase stakeholder trust in AI by 67% (MIT, 2025)
- EU AI Act affects 40% of enterprise AI as 'high-risk'
Table of Contents
- Executive Summary
- Understanding Your Problem Type
- Data Characteristics Assessment
- Model Selection by Problem Category
- Performance vs. Interpretability Tradeoffs
- Operational Constraints and Deployment
- Model Evaluation and Comparison
- Decision Framework: Putting It All Together
- Key Statistics
- Conclusion & Recommendations
- References
Who Should Read This
- ML engineers evaluating model architectures
- Data science team leads planning projects
- CTOs making build-vs-buy decisions for AI
- Technical product managers scoping ML features
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