Grid search con intervalli di iperparametri

Nel capitolo 1 hai imparato a usare la funzione expand.grid() per definire manualmente un insieme di iperparametri. La stessa funzione può essere usata anche per definire una griglia con intervalli di iperparametri.

Il dataset voters_train_data è stato caricato per te, così come i pacchetti caret e tictoc.

Questo esercizio fa parte del corso

Ottimizzazione degli iperparametri in R

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Esercizio pratico interattivo

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# Define the grid with hyperparameter ranges
big_grid <- expand.grid(___ = ___(___ = 1, ___ = 5, by = ___),
                        decay = c(0, 1))
big_grid

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Questo esercizio fa parte del corso

Ottimizzazione degli iperparametri in R

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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: Ottimizzazione degli iperparametri con caret Exercise 2: Trovare gli iperparametri Exercise 3: Ricerca a griglia cartesiana in caret Exercise 4: Rappresenta graficamente l'output del modello con iperparametri Exercise 5: Grid vs. Random Search Exercise 6: Grid search con intervalli di iperparametri

Esercizio in corso

Exercise 7: Trova l'opzione di train() per la random search Exercise 8: Random search con caret Exercise 9: Ricampionamento adattivo Exercise 10: Vantaggi dell'Adaptive Resampling Exercise 11: Adaptive Resampling con 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