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Master Data Science step-by-step
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Module 7
Model Tuning and Selection
1
Parameters vs. Hyperparameters
Explain the difference between model parameters and hyperparameters.
2
GridSearchCV for Hyperparameter Tuning
Tune model hyperparameters systematically using GridSearchCV.
3
Efficient Hyperparameter Tuning with RandomizedSearchCV
Tune model hyperparameters efficiently using RandomizedSearchCV.
4
ROC Curves and AUC Score for Model Comparison
Plot and interpret ROC curves and use the AUC score to compare classification models.
5
Comparing Models with Cross-Validated Metrics
Systematically compare multiple models using cross-validated metrics.
6
Model Selection and Justification
Select a final model and justify the choice based on performance and project goals.
Previous module
Supervised Learning: Classification
Next module
Advanced Supervised Learning: Ensemble Methods