Wrap-up

1. Congratulations!

Congrats! You made it through the course!

2. What you've learned in this course

In this course, you learned what hyperparameters are, how they are different from model parameters and why it is useful to tune them in your machine learning models. You also learned HOW to tune hyperparameters with the R packages caret mlr and h2o.

3. Terms you can understand and apply

Specifically, you learned what the following terms mean and how to apply them: - Cartesian grid search for tuning all possible combinations of a hyperparameter set - Random Search for tuning with random sampling from a hyperparameter set - Adaptive Resampling for more efficient tuning of a hyperparameter search space - and Automatic Machine Learning for quick and easy model and hyperparameter optimization. - You know how to evaluate tuning results by examining and visualizing performance metrics - and how to use performance metrics in early stopping

4. How you can use this knowledge

With these techniques, you should now be able to find the best (or most optimal) hyperparameter combinations for your own models. You got to know three R packages for machine learning and hyperparameter tuning, so you will now have a feeling for how they work and how they differ. Maybe you even found a favorite, which you prefer to work with from now on. So, where do you start, if you want to go further? For additional information and tutorials, read the package manuals and vignettes. And of course: try it out! Maybe you already have data of your own. Or look for example datasets at the UC Irvine Machine Learning Repository or Kaggle. Follow the links to get there.

5. Thank you and have fun!

With this, I want to thank you for completing this course! I hope you benefit from what you've learned and that you will have a lot of fun trying things out! Bye!

This exercise is part of the course

Hyperparameter Tuning in R

AdvancedSkill Level

4.9+

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Why do we use the strange word "hyperparameter"? What makes it hyper? Here, you will understand what model parameters are, and why they are different from hyperparameters in machine learning. You will then see why we would want to tune them and how the default setting of caret automatically includes hyperparameter tuning.

Exercise 1: Parameters vs hyperparameters Exercise 2: Model parameters vs. hyperparameters Exercise 3: Hyperparameters in linear models Exercise 4: What are the coefficients?Exercise 5: Recap of machine learning basics Exercise 6: Machine learning with caret Exercise 7: Resampling schemes Exercise 8: Hyperparameter tuning in caret Exercise 9: Hyperparameters in Stochastic Gradient Boosting Exercise 10: Changing the number of hyperparameters to tune Exercise 11: Tune hyperparameters manually

In this chapter, you will learn how to tune hyperparameters with a Cartesian grid. Then, you will implement faster and more efficient approaches. You will use Random Search and adaptive resampling to tune the parameter grid, in a way that concentrates on values in the neighborhood of the optimal settings.

Exercise 1: Hyperparameter tuning in caret Exercise 2: Finding hyperparameters Exercise 3: Cartesian grid search in caret Exercise 4: Plot hyperparameter model output Exercise 5: Grid vs. Random Search Exercise 6: Grid search with range of hyperparameters Exercise 7: Find train() option for random search Exercise 8: Random search with caret Exercise 9: Adaptive resampling Exercise 10: Advantages of Adaptive Resampling Exercise 11: Adaptive Resampling with caret

Here, you will use another package for machine learning that has very convenient hyperparameter tuning functions. You will define a Cartesian grid or perform Random Search, as well as advanced techniques. You will also learn different ways to plot and evaluate models with different hyperparameters.

Exercise 1: Machine learning with mlr Exercise 2: Machine Learning with mlr Exercise 3: Modeling with mlr Exercise 4: Grid and random search with mlr Exercise 5: Random search with mlr Exercise 6: Perform hyperparameter tuning with mlr Exercise 7: Evaluating hyperparameters with mlr Exercise 8: Why to evaluate tuning?Exercise 9: Evaluating hyperparameter tuning results Exercise 10: Advanced tuning with mlr Exercise 11: Define advanced tuning controls Exercise 12: Define aggregated measures Exercise 13: Setting hyperparameters

In this final chapter, you will use h2o, another package for machine learning with very convenient hyperparameter tuning functions. You will use it to train different models and define a Cartesian grid. Then, You will implement a Random Search use stopping criteria. Finally, you will learn AutoML, an h2o interface which allows for very fast and convenient model and hyperparameter tuning with just one function.

Exercise 1: Machine learning with h2o Exercise 2: Prepare data for modelling with h2o Exercise 3: Modeling with h2o Exercise 4: Grid and random search with h2o Exercise 5: Grid search with h2o Exercise 6: Random search with h2o Exercise 7: Stopping criteria Exercise 8: Automatic machine learning with H2O Exercise 9: AutoML in h2o Exercise 10: Scoring the leaderboard Exercise 11: Extract h2o models and evaluate performance Exercise 12: Wrap-up

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