Model Selection and Tuning

Learn how to select the optimum model and tune hyperparameters in Kaggle competitions.


  • Learn how the k-nearest neighbors and random forest algorithms work.
  • Learn about hyperparameters and how to select the hyperparameters that give the best prediction.
  • Learn how to compare differrent algorithms to improve the accuracy of your predictions.

Mission Outline

1. Introducing Model Selection
2. Training a Baseline Model
3. Training a Model using K-Nearest Neighbors
4. Exploring Different K Values
5. Automating Hyperparameter Optimization with Grid Search
6. Submitting K-Nearest Neighbors Predictions to Kaggle
7. Introducing Random Forests
8. Tuning our Random Forests Model with GridSearch
9. Submitting Random Forest Predictions to Kaggle
10. Next Steps
11. Takeaways


Course Info:


The median completion time for this course is 5.9 hours. View Details

This course requires a premium subscription, and includes three missions and one guided project.  It is the 28th course in the Data Scientist in Python path.


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