Model Selection Techniques

Model selection means comparing different algorithms on the same dataset and picking the one that performs best for your specific problem.

• Cross-validation: Test each model across multiple data splits, not just one, to get a reliable performance estimate.
• Metric Comparison: Judge every model using the same evaluation metric to keep the comparison fair.
• Training Time: Consider how long a model takes to train, since a slightly less accurate but faster model can be more practical.
• Interpretability: Prefer a simpler, explainable model when accuracy differences between options are small.
• Business Fit: Choose the model that best aligns with real-world constraints, not just the one with the highest score on paper.