Model Tuning and Pipelines - Recap
Key Takeaways
The key takeaways from this section include:
- Machine learning pipelines create a nice workflow to combine data manipulations, preprocessing, and modeling
- Machine learning pipelines can be used along with grid search to evaluate several parameter settings
- Grid search can considerably blow up computation time when computing for several parameters along with cross-validation
- Some models are very sensitive to hyperparameter changes, so they should be chosen with care, and even with big grids a good outcome isn't always guaranteed