Model Selection
Data Science · University of Applied Sciences · Machine Learning
This unit delves into the art of model selection, equipping students with the knowledge and skills to optimize model performance. Through hyperparameter tuning, regularization techniques, and ensemble methods, students will gain a comprehensive understanding of how to select and refine models for real-world applications.
Zum Üben gibt es 52 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Introduction to Model Selection
- Hyperparameter Tuning: Grid Search
- Random Search
- Bayesian Optimization
- Regularization Techniques: L1
- L2
- Elastic Net
- Ensemble Methods: Bagging
- Boosting
- Random Forests
- Cross-Validation and Model Evaluation
- Bias-Variance Tradeoff
Stichworte
- Model Selection
- Hyperparameter Tuning
- Regularization
- Ensemble Methods
- Cross-Validation
- Overfitting
- Underfitting