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.

Parameters vs hyperparameters

Model parameters vs. hyperparameters

Hyperparameters in linear models

What are the coefficients?

Recap of machine learning basics

Machine learning with caret

Resampling schemes

Hyperparameter tuning in caret

Hyperparameters in Stochastic Gradient Boosting

Changing the number of hyperparameters to tune

Tune hyperparameters manually

Introduction to hyperparameters

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. 

Finding hyperparameters

Cartesian grid search in caret

Plot hyperparameter model output

Grid vs. Random Search

Grid search with range of hyperparameters

Find train() option for random search

Random search with caret

Adaptive resampling

Advantages of Adaptive Resampling

Adaptive Resampling with caret

Hyperparameter tuning 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.

Machine Learning con mlr

Modeling con mlr

Grid e random search con mlr

Random search con mlr

Esegui l'hyperparameter tuning con mlr

Valutare gli iperparametri con mlr

Perché valutare il tuning?

Valutare i risultati del tuning degli iperparametri

Ottimizzazione avanzata con mlr

Definisci controlli di tuning avanzati

Definisci misure aggregate

Impostare gli iperparametri

Hyperparameter tuning with mlr

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.

Machine learning with h2o

Prepare data for modelling with h2o

Modeling with h2o

Grid and random search with h2o

Grid search with h2o

Random search with h2o

Stopping criteria

Automatic machine learning with H2O

AutoML in h2o

Scoring the leaderboard

Extract h2o models and evaluate performance

Wrap-up

Hyperparameter tuning with h2o

Bc test data

Bc train data

Breast cancer data

Breast cancer data orig

Datasets descriptions

Knowledge data

Knowledge orig

Knowledge test data

Knowledge train data

Seeds data

Seeds dataset

Seeds test data

Seeds train data

Voters data

Voters orig

Voters test data

Voters train data

Per molti problemi di Machine Learning, eseguire un modello “così com’è” e ottenere una previsione non basta: vuoi il modello migliore, con la massima accuratezza. Un modo per perfezionarlo è l’ottimizzazione degli iperparametri, cioè la scelta delle impostazioni ideali per quello specifico modello. In questo corso userai i pacchetti caret, mlr e h2o per trovare in modo efficiente la combinazione ottimale di iperparametri tramite grid search, random search, ricampionamento adattivo e automatic machine learning (AutoML). Inoltre, lavorerai con diversi insiemi di dati e ottimizzerai vari modelli di apprendimento supervisionato, come random forest, gradient boosting machine, support vector machine e persino reti neurali. Pronto a fare tuning!

Machine Learning with caret in R

Usa caret, mlr e h2o per trovare iperparametri ottimali con grid search, random search e AutoML.

Ottimizzazione degli iperparametri in R

Scopri come sistemare gli iperparametri del tuo modello per avere i migliori risultati di previsione.

Valutare gli iperparametri con mlr

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