Model Selection in Machine Learning
Model selection is a crucial step in the machine learning pipeline that deals with choosing the best model among a set of candidate models.
The process involves using techniques such as cross-validation to estimate the performance of each model and selecting the one that best generalizes to unseen data. 🤖🔍
Why is model selection important?
Choosing the right model is essential to achieving high predictive accuracy and preventing issues like overfitting or underfitting. With the plethora of models available, it's important to understand the trade-offs and real-world implications of each model's performance.
Common Techniques
- Cross-Validation
- Grid Search
- Random Search
- Bayesian Optimization
Want to learn more? Check out our comprehensive guide on model selection.