Model parameters vs. hyperparameters

In order to perform hyperparameter tuning, it is important to really understand what hyperparameters are (and what they are not). So let's look at model parameters versus hyperparameters in detail.

Note: The Breast Cancer Wisconsin (Diagnostic) dataset has been loaded as breast_cancer_data for you.

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Hyperparameter Tuning in R

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Use this dataset to fit a linear model with concavity_mean as response and symmetry_mean as predictor variable.
Look at the summary() of this linear model.
Extract the coefficients.

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이 예제를 이 샘플 코드를 완성하여 풀어보세요.

# Fit a linear model on the breast_cancer_data.
linear_model <- ___(___)

# Look at the summary of the linear_model.
___

# Extract the coefficients.
___

코드 편집 및 실행

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Hyperparameter Tuning in R

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4.8+

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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

현재 연습

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